Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis (Editors)

Anthology ID:: L16-1
Month:: May
Year:: 2016
Address:: Portorož, Slovenia
Venue:: LREC
SIG:
Publisher:: European Language Resources Association (ELRA)
URL:: https://aclanthology.org/L16-1
DOI:
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Evaluating Machine Translation in a Usage Scenario
Rosa Gaudio | Aljoscha Burchardt | António Branco

In this document we report on a user-scenario-based evaluation aiming at assessing the performance of machine translation (MT) systems in a real context of use. We describe a sequel of experiments that has been performed to estimate the usefulness of MT and to test if improvements of MT technology lead to better performance in the usage scenario. One goal is to find the best methodology for evaluating the eventual benefit of a machine translation system in an application. The evaluation is based on the QTLeap corpus, a novel multilingual language resource that was collected through a real-life support service via chat. It is composed of naturally occurring utterances produced by users while interacting with a human technician providing answers. The corpus is available in eight different languages: Basque, Bulgarian, Czech, Dutch, English, German, Portuguese and Spanish.

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Using BabelNet to Improve OOV Coverage in SMT
Jinhua Du | Andy Way | Andrzej Zydron

Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods ― using direct training data, domain-adaptation techniques and the BabelNet API ― are proposed in this paper to obtain translations for OOVs to improve system performance. Experimental results on English―Polish and English―Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.

The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. More specifically, videolectures, assignments, and MOOC forum text is automatically translated from English into eleven European and BRIC languages. Unlike previous approaches to machine translation, the output quality in TraMOOC relies on a multimodal evaluation schema that involves crowdsourcing, error type markup, an error taxonomy for translation model comparison, and implicit evaluation via text mining, i.e. entity recognition and its performance comparison between the source and the translated text, and sentiment analysis on the students’ forum posts. Finally, the evaluation output will result in more and better quality in-domain parallel data that will be fed back to the translation engine for higher quality output. The translation service will be incorporated into the Iversity MOOC platform and into the VideoLectures.net digital library portal.

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The Trials and Tribulations of Predicting Post-Editing Productivity
Lena Marg

While an increasing number of (automatic) metrics is available to assess the linguistic quality of machine translations, their interpretation remains cryptic to many users, specifically in the translation community. They are clearly useful for indicating certain overarching trends, but say little about actual improvements for translation buyers or post-editors. However, these metrics are commonly referenced when discussing pricing and models, both with translation buyers and service providers. With the aim of focusing on automatic metrics that are easier to understand for non-research users, we identified Edit Distance (or Post-Edit Distance) as a good fit. While Edit Distance as such does not express cognitive effort or time spent editing machine translation suggestions, we found that it correlates strongly with the productivity tests we performed, for various language pairs and domains. This paper aims to analyse Edit Distance and productivity data on a segment level based on data gathered over some years. Drawing from these findings, we want to then explore how Edit Distance could help in predicting productivity on new content. Some further analysis is proposed, with findings to be presented at the conference.

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PE2rr Corpus: Manual Error Annotation of Automatically Pre-annotated MT Post-edits
Maja Popović | Mihael Arčan

We present a freely available corpus containing source language texts from different domains along with their automatically generated translations into several distinct morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations. We believe that the corpus will be useful for many different applications. The main advantage of the approach used for creation of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent tasks, although naturally they are not. We also show benefits of coupling automatic and manual error classification which facilitates the complex manual error annotation task as well as the development of automatic error classification tools. In addition, the approach facilitates annotation of language pair related issues.

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Sentiment Lexicons for Arabic Social Media
Saif Mohammad | Mohammad Salameh | Svetlana Kiritchenko

Existing Arabic sentiment lexicons have low coverage―with only a few thousand entries. In this paper, we present several large sentiment lexicons that were automatically generated using two different methods: (1) by using distant supervision techniques on Arabic tweets, and (2) by translating English sentiment lexicons into Arabic using a freely available statistical machine translation system. We compare the usefulness of new and old sentiment lexicons in the downstream application of sentence-level sentiment analysis. Our baseline sentiment analysis system uses numerous surface form features. Nonetheless, the system benefits from using additional features drawn from sentiment lexicons. The best result is obtained using the automatically generated Dialectal Hashtag Lexicon and the Arabic translations of the NRC Emotion Lexicon (accuracy of 66.6%). Finally, we describe a qualitative study of the automatic translations of English sentiment lexicons into Arabic, which shows that about 88% of the automatically translated entries are valid for English as well. Close to 10% of the invalid entries are caused by gross mistranslations, close to 40% by translations into a related word, and about 50% by differences in how the word is used in Arabic.

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A Language Independent Method for Generating Large Scale Polarity Lexicons
Giuseppe Castellucci | Danilo Croce | Roberto Basili

Sentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts. Many approaches in literature are based on resources that model the prior polarity of words or multi-word expressions, i.e. a polarity lexicon. Such resources are defined by teams of annotators, i.e. a manual annotation is provided to associate emotional or sentiment facets to the lexicon entries. The development of such lexicons is an expensive and language dependent process, making them often not covering all the linguistic sentiment phenomena. Moreover, once a lexicon is defined it can hardly be adopted in a different language or even a different domain. In this paper, we present several Distributional Polarity Lexicons (DPLs), i.e. large-scale polarity lexicons acquired with an unsupervised methodology based on Distributional Models of Lexical Semantics. Given a set of heuristically annotated sentences from Twitter, we transfer the sentiment information from sentences to words. The approach is mostly unsupervised, and experimental evaluations on Sentiment Analysis tasks in two languages show the benefits of the generated resources. The generated DPLs are publicly available in English and Italian.

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Sentiment Analysis in Social Networks through Topic modeling
Debashis Naskar | Sidahmed Mokaddem | Miguel Rebollo | Eva Onaindia

In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) we use ANEW, a lexical dictionary to identify affective emotional feelings associated to a message according to the Russell’s model of affection; (2) we design a topic modeling mechanism called Sent_LDA, based on the Latent Dirichlet Allocation (LDA) generative model, which allows us to find the topic distribution in a general conversation and we associate topics with emotions; (3) we detect communities in the network according to the density and frequency of the messages among the users; and (4) we compare the sentiments of the communities by using the Russell’s model of affect versus polarity and we measure the extent to which topic distribution strengthen likeness in the sentiments of the users of a community. This works contributes with a topic modeling methodology to analyze the sentiments in conversations that take place in social networks.

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A Comparison of Domain-based Word Polarity Estimation using different Word Embeddings
Aitor García Pablos | Montse Cuadros | German Rigau

A key point in Sentiment Analysis is to determine the polarity of the sentiment implied by a certain word or expression. In basic Sentiment Analysis systems this sentiment polarity of the words is accounted and weighted in different ways to provide a degree of positivity/negativity. Currently words are also modelled as continuous dense vectors, known as word embeddings, which seem to encode interesting semantic knowledge. With regard to Sentiment Analysis, word embeddings are used as features to more complex supervised classification systems to obtain sentiment classifiers. In this paper we compare a set of existing sentiment lexicons and sentiment lexicon generation techniques. We also show a simple but effective technique to calculate a word polarity value for each word in a domain using existing continuous word embeddings generation methods. Further, we also show that word embeddings calculated on in-domain corpus capture the polarity better than the ones calculated on general-domain corpus.

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Could Speaker, Gender or Age Awareness be beneficial in Speech-based Emotion Recognition?
Maxim Sidorov | Alexander Schmitt | Eugene Semenkin | Wolfgang Minker

Emotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs). The emotional hypothesis of the current response of an end-user might be utilised by the dialogue manager component in order to change the SDS strategy which could result in a quality enhancement. In this study additional speaker-related information is used to improve the performance of the speech-based ER process. The analysed information is the speaker identity, gender and age of a user. Two schemes are described here, namely, using additional information as an independent variable within the feature vector and creating separate emotional models for each speaker, gender or age-cluster independently. The performances of the proposed approaches were compared against the baseline ER system, where no additional information has been used, on a number of emotional speech corpora of German, English, Japanese and Russian. The study revealed that for some of the corpora the proposed approach significantly outperforms the baseline methods with a relative difference of up to 11.9%.

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Discriminative Analysis of Linguistic Features for Typological Study
Hiroya Takamura | Ryo Nagata | Yoshifumi Kawasaki

We address the task of automatically estimating the missing values of linguistic features by making use of the fact that some linguistic features in typological databases are informative to each other. The questions to address in this work are (i) how much predictive power do features have on the value of another feature? (ii) to what extent can we attribute this predictive power to genealogical or areal factors, as opposed to being provided by tendencies or implicational universals? To address these questions, we conduct a discriminative or predictive analysis on the typological database. Specifically, we use a machine-learning classifier to estimate the value of each feature of each language using the values of the other features, under different choices of training data: all the other languages, or all the other languages except for the ones having the same origin or area with the target language.

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POS-tagging of Historical Dutch
Dieuwke Hupkes | Rens Bod

We present a study of the adequacy of current methods that are used for POS-tagging historical Dutch texts, as well as an exploration of the influence of employing different techniques to improve upon the current practice. The main focus of this paper is on (unsupervised) methods that are easily adaptable for different domains without requiring extensive manual input. It was found that modernising the spelling of corpora prior to tagging them with a tagger trained on contemporary Dutch results in a large increase in accuracy, but that spelling normalisation alone is not sufficient to obtain state-of-the-art results. The best results were achieved by training a POS-tagger on a corpus automatically annotated by projecting (automatically assigned) POS-tags via word alignments from a contemporary corpus. This result is promising, as it was reached without including any domain knowledge or context dependencies. We argue that the insights of this study combined with semi-supervised learning techniques for domain adaptation can be used to develop a general-purpose diachronic tagger for Dutch.

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A Language Resource of German Errors Written by Children with Dyslexia
Maria Rauschenberger | Luz Rello | Silke Füchsel | Jörg Thomaschewski

In this paper we present a language resource for German, composed of a list of 1,021 unique errors extracted from a collection of texts written by people with dyslexia. The errors were annotated with a set of linguistic characteristics as well as visual and phonetic features. We present the compilation and the annotation criteria for the different types of dyslexic errors. This language resource has many potential uses since errors written by people with dyslexia reflect their difficulties. For instance, it has already been used to design language exercises to treat dyslexia in German. To the best of our knowledge, this is first resource of this kind in German.

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CItA: an L1 Italian Learners Corpus to Study the Development of Writing Competence
Alessia Barbagli | Pietro Lucisano | Felice Dell’Orletta | Simonetta Montemagni | Giulia Venturi

In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school. The corpus was built in the framework of an interdisciplinary study jointly carried out by computational linguistics and experimental pedagogists and aimed at tracking the development of written language competence over the years and students’ background information.

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If You Even Don’t Have a Bit of Bible: Learning Delexicalized POS Taggers
Zhiwei Yu | David Mareček | Zdeněk Žabokrtský | Daniel Zeman

Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Various unsupervised and semi-supervised methods have been proposed to tag an unseen language. However, many of them require some partial understanding of the target language because they rely on dictionaries or parallel corpora such as the Bible. In this paper, we propose a different method named delexicalized tagging, for which we only need a raw corpus of the target language. We transfer tagging models trained on annotated corpora of one or more resource-rich languages. We employ language-independent features such as word length, frequency, neighborhood entropy, character classes (alphabetic vs. numeric vs. punctuation) etc. We demonstrate that such features can, to certain extent, serve as predictors of the part of speech, represented by the universal POS tag.

The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS’s development cycle.

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Creating Annotated Dialogue Resources: Cross-domain Dialogue Act Classification
Dilafruz Amanova | Volha Petukhova | Dietrich Klakow

This paper describes a method to automatically create dialogue resources annotated with dialogue act information by reusing existing dialogue corpora. Numerous dialogue corpora are available for research purposes and many of them are annotated with dialogue act information that captures the intentions encoded in user utterances. Annotated dialogue resources, however, differ in various respects: data collection settings and modalities used, dialogue task domains and scenarios (if any) underlying the collection, number and roles of dialogue participants involved and dialogue act annotation schemes applied. The presented study encompasses three phases of data-driven investigation. We, first, assess the importance of various types of features and their combinations for effective cross-domain dialogue act classification. Second, we establish the best predictive model comparing various cross-corpora training settings. Finally, we specify models adaptation procedures and explore late fusion approaches to optimize the overall classification decision taking process. The proposed methodology accounts for empirically motivated and technically sound classification procedures that may reduce annotation and training costs significantly.

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Towards a Multi-dimensional Taxonomy of Stories in Dialogue
Kathryn J. Collins | David Traum

In this paper, we present a taxonomy of stories told in dialogue. We based our scheme on prior work analyzing narrative structure and method of telling, relation to storyteller identity, as well as some categories particular to dialogue, such as how the story gets introduced. Our taxonomy currently has 5 major dimensions, with most having sub-dimensions - each dimension has an associated set of dimension-specific labels. We adapted an annotation tool for this taxonomy and have annotated portions of two different dialogue corpora, Switchboard and the Distress Analysis Interview Corpus. We present examples of some of the tags and concepts with stories from Switchboard, and some initial statistics of frequencies of the tags.

PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings. The corpus is multilingual, with English and German sections, and overall comprises more than 20000 utterances. The dialogues are fully transcribed and annotated with referring expressions mapped to objects in corresponding visual scenes, which makes the corpus a rich resource for research on spoken referring expressions in generation and resolution. The corpus includes several sub-corpora that correspond to different dialogue situations where parameters related to interactivity, visual access, and verbal channel have been manipulated in systematic ways. The corpus thus lends itself to very targeted studies of reference in spontaneous dialogue.

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Transfer of Corpus-Specific Dialogue Act Annotation to ISO Standard: Is it worth it?
Shammur Absar Chowdhury | Evgeny Stepanov | Giuseppe Riccardi

Spoken conversation corpora often adapt existing Dialogue Act (DA) annotation specifications, such as DAMSL, DIT++, etc., to task specific needs, yielding incompatible annotations; thus, limiting corpora re-usability. Recently accepted ISO standard for DA annotation – Dialogue Act Markup Language (DiAML) – is designed as domain and application independent. Moreover, the clear separation of dialogue dimensions and communicative functions, coupled with the hierarchical organization of the latter, allows for classification at different levels of granularity. However, re-annotating existing corpora with the new scheme might require significant effort. In this paper we test the utility of the ISO standard through comparative evaluation of the corpus-specific legacy and the semi-automatically transferred DiAML DA annotations on supervised dialogue act classification task. To test the domain independence of the resulting annotations, we perform cross-domain and data aggregation evaluation. Compared to the legacy annotation scheme, on the Italian LUNA Human-Human corpus, the DiAML annotation scheme exhibits better cross-domain and data aggregation classification performance, while maintaining comparable in-domain performance.

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WikiCoref: An English Coreference-annotated Corpus of Wikipedia Articles
Abbas Ghaddar | Phillippe Langlais

This paper presents WikiCoref, an English corpus annotated for anaphoric relations, where all documents are from the English version of Wikipedia. Our annotation scheme follows the one of OntoNotes with a few disparities. We annotated each markable with coreference type, mention type and the equivalent Freebase topic. Since most similar annotation efforts concentrate on very specific types of written text, mainly newswire, there is a lack of resources for otherwise over-used Wikipedia texts. The corpus described in this paper addresses this issue. We present a freely available resource we initially devised for improving coreference resolution algorithms dedicated to Wikipedia texts. Our corpus has no restriction on the topics of the documents being annotated, and documents of various sizes have been considered for annotation.

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Exploitation of Co-reference in Distributional Semantics
Dominik Schlechtweg

The aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that similar words have similar contexts. Deducing meaning from the distribution of words is interesting as it can be done automatically on large amounts of freely available raw text. It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e.g., syntactic―information to improve distributional semantic models. In contrast, less attention has been paid to semantic information in the research community. One reason for this is that the extraction of semantic information from raw text is a complex, elaborate matter and in great parts not yet satisfyingly solved. Recently, however, there have been successful attempts to integrate a certain kind of semantic information, i.e., co-reference. Two basically different kinds of information contributed by co-reference with respect to the distribution of words will be identified. We will then focus on one of these and examine its general potential to improve distributional semantic models as well as certain more specific hypotheses.

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Adapting an Entity Centric Model for Portuguese Coreference Resolution
Evandro Fonseca | Renata Vieira | Aline Vanin

This paper presents the adaptation of an Entity Centric Model for Portuguese coreference resolution, considering 10 named entity categories. The model was evaluated on named e using the HAREM Portuguese corpus and the results are 81.0% of precision and 58.3% of recall overall, the resulting system is freely available

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IMS HotCoref DE: A Data-driven Co-reference Resolver for German
Ina Roesiger | Jonas Kuhn

This paper presents a data-driven co-reference resolution system for German that has been adapted from IMS HotCoref, a co-reference resolver for English. It describes the difficulties when resolving co-reference in German text, the adaptation process and the features designed to address linguistic challenges brought forth by German. We report performance on the reference dataset TüBa-D/Z and include a post-task SemEval 2010 evaluation, showing that the resolver achieves state-of-the-art performance. We also include ablation experiments that indicate that integrating linguistic features increases results. The paper also describes the steps and the format necessary to use the resolver on new texts. The tool is freely available for download.

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Coreference Annotation Scheme and Relation Types for Hindi
Vandan Mujadia | Palash Gupta | Dipti Misra Sharma

This paper describes a coreference annotation scheme, coreference annotation specific issues and their solutions through our proposed annotation scheme for Hindi. We introduce different co-reference relation types between continuous mentions of the same coreference chain such as “Part-of”, “Function-value pair” etc. We used Jaccard similarity based Krippendorff‘s’ alpha to demonstrate consistency in annotation scheme, annotation and corpora. To ease the coreference annotation process, we built a semi-automatic Coreference Annotation Tool (CAT). We also provide statistics of coreference annotation on Hindi Dependency Treebank (HDTB).

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Coreference in Prague Czech-English Dependency Treebank
Anna Nedoluzhko | Michal Novák | Silvie Cinková | Marie Mikulová | Jiří Mírovský

We present coreference annotation on parallel Czech-English texts of the Prague Czech-English Dependency Treebank (PCEDT). The paper describes innovations made to PCEDT 2.0 concerning coreference, as well as coreference information already present there. We characterize the coreference annotation scheme, give the statistics and compare our annotation with the coreference annotation in Ontonotes and Prague Dependency Treebank for Czech. We also present the experiments made using this corpus to improve the alignment of coreferential expressions, which helps us to collect better statistics of correspondences between types of coreferential relations in Czech and English. The corpus released as PCEDT 2.0 Coref is publicly available.

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Sieve-based Coreference Resolution in the Biomedical Domain
Dane Bell | Gus Hahn-Powell | Marco A. Valenzuela-Escárcega | Mihai Surdeanu

We describe challenges and advantages unique to coreference resolution in the biomedical domain, and a sieve-based architecture that leverages domain knowledge for both entity and event coreference resolution. Domain-general coreference resolution algorithms perform poorly on biomedical documents, because the cues they rely on such as gender are largely absent in this domain, and because they do not encode domain-specific knowledge such as the number and type of participants required in chemical reactions. Moreover, it is difficult to directly encode this knowledge into most coreference resolution algorithms because they are not rule-based. Our rule-based architecture uses sequentially applied hand-designed “sieves”, with the output of each sieve informing and constraining subsequent sieves. This architecture provides a 3.2% increase in throughput to our Reach event extraction system with precision parallel to that of the stricter system that relies solely on syntactic patterns for extraction.

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Annotating Characters in Literary Corpora: A Scheme, the CHARLES Tool, and an Annotated Novel
Hardik Vala | Stefan Dimitrov | David Jurgens | Andrew Piper | Derek Ruths

Characters form the focus of various studies of literary works, including social network analysis, archetype induction, and plot comparison. The recent rise in the computational modelling of literary works has produced a proportional rise in the demand for character-annotated literary corpora. However, automatically identifying characters is an open problem and there is low availability of literary texts with manually labelled characters. To address the latter problem, this work presents three contributions: (1) a comprehensive scheme for manually resolving mentions to characters in texts. (2) A novel collaborative annotation tool, CHARLES (CHAracter Resolution Label-Entry System) for character annotation and similiar cross-document tagging tasks. (3) The character annotations resulting from a pilot study on the novel Pride and Prejudice, demonstrating the scheme and tool facilitate the efficient production of high-quality annotations. We expect this work to motivate the further production of annotated literary corpora to help meet the demand of the community.

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Error Typology and Remediation Strategies for Requirements Written in English by Non-Native Speakers
Marie Garnier | Patrick Saint-Dizier

In most international industries, English is the main language of communication for technical documents. These documents are designed to be as unambiguous as possible for their users. For international industries based in non-English speaking countries, the professionals in charge of writing requirements are often non-native speakers of English, who rarely receive adequate training in the use of English for this task. As a result, requirements can contain a relatively large diversity of lexical and grammatical errors, which are not eliminated by the use of guidelines from controlled languages. This article investigates the distribution of errors in a corpus of requirements written in English by native speakers of French. Errors are defined on the basis of grammaticality and acceptability principles, and classified using comparable categories. Results show a high proportion of errors in the Noun Phrase, notably through modifier stacking, and errors consistent with simplification strategies. Comparisons with similar corpora in other genres reveal the specificity of the distribution of errors in requirements. This research also introduces possible applied uses, in the form of strategies for the automatic detection of errors, and in-person training provided by certification boards in requirements authoring.

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Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario
Lena Keiper | Andrea Horbach | Stefan Thater

We present a novel method to automatically improve the accurracy of part-of-speech taggers on learner language. The key idea underlying our approach is to exploit the structure of a typical language learner task and automatically induce POS information for out-of-vocabulary (OOV) words. To evaluate the effectiveness of our approach, we add manual POS and normalization information to an existing language learner corpus. Our evaluation shows an increase in accurracy from 72.4% to 81.5% on OOV words.

We present a new resource for Swedish, SweLL, a corpus of Swedish Learner essays linked to learners’ performance according to the Common European Framework of Reference (CEFR). SweLL consists of three subcorpora ― SpIn, SW1203 and Tisus, collected from three different educational establishments. The common metadata for all subcorpora includes age, gender, native languages, time of residence in Sweden, type of written task. Depending on the subcorpus, learner texts may contain additional information, such as text genres, topics, grades. Five of the six CEFR levels are represented in the corpus: A1, A2, B1, B2 and C1 comprising in total 339 essays. C2 level is not included since courses at C2 level are not offered. The work flow consists of collection of essays and permits, essay digitization and registration, meta-data annotation, automatic linguistic annotation. Inter-rater agreement is presented on the basis of SW1203 subcorpus. The work on SweLL is still ongoing with more that 100 essays waiting in the pipeline. This article both describes the resource and the “how-to” behind the compilation of SweLL.

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SVALex: a CEFR-graded Lexical Resource for Swedish Foreign and Second Language Learners
Thomas François | Elena Volodina | Ildikó Pilán | Anaïs Tack

The paper introduces SVALex, a lexical resource primarily aimed at learners and teachers of Swedish as a foreign and second language that describes the distribution of 15,681 words and expressions across the Common European Framework of Reference (CEFR). The resource is based on a corpus of coursebook texts, and thus describes receptive vocabulary learners are exposed to during reading activities, as opposed to productive vocabulary they use when speaking or writing. The paper describes the methodology applied to create the list and to estimate the frequency distribution. It also discusses some characteristics of the resulting resource and compares it to other lexical resources for Swedish. An interesting feature of this resource is the possibility to separate the wheat from the chaff, identifying the core vocabulary at each level, i.e. vocabulary shared by several coursebook writers at each level, from peripheral vocabulary which is used by the minority of the coursebook writers.

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Detecting Word Usage Errors in Chinese Sentences for Learning Chinese as a Foreign Language
Yow-Ting Shiue | Hsin-Hsi Chen

Automated grammatical error detection, which helps users improve their writing, is an important application in NLP. Recently more and more people are learning Chinese, and an automated error detection system can be helpful for the learners. This paper proposes n-gram features, dependency count features, dependency bigram features, and single-character features to determine if a Chinese sentence contains word usage errors, in which a word is written as a wrong form or the word selection is inappropriate. With marking potential errors on the level of sentence segments, typically delimited by punctuation marks, the learner can try to correct the problems without the assistant of a language teacher. Experiments on the HSK corpus show that the classifier combining all sets of features achieves an accuracy of 0.8423. By utilizing certain combination of the sets of features, we can construct a system that favors precision or recall. The best precision we achieve is 0.9536, indicating that our system is reliable and seldom produces misleading results.

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LibN3L:A Lightweight Package for Neural NLP
Meishan Zhang | Jie Yang | Zhiyang Teng | Yue Zhang

We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.

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Evaluating Lexical Simplification and Vocabulary Knowledge for Learners of French: Possibilities of Using the FLELex Resource
Anaïs Tack | Thomas François | Anne-Laure Ligozat | Cédrick Fairon

This study examines two possibilities of using the FLELex graded lexicon for the automated assessment of text complexity in French as a foreign language learning. From the lexical frequency distributions described in FLELex, we derive a single level of difficulty for each word in a parallel corpus of original and simplified texts. We then use this data to automatically address the lexical complexity of texts in two ways. On the one hand, we evaluate the degree of lexical simplification in manually simplified texts with respect to their original version. Our results show a significant simplification effect, both in the case of French narratives simplified for non-native readers and in the case of simplified Wikipedia texts. On the other hand, we define a predictive model which identifies the number of words in a text that are expected to be known at a particular learning level. We assess the accuracy with which these predictions are able to capture actual word knowledge as reported by Dutch-speaking learners of French. Our study shows that although the predictions seem relatively accurate in general (87.4% to 92.3%), they do not yet seem to cover the learners’ lack of knowledge very well.

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A Shared Task for Spoken CALL?
Claudia Baur | Johanna Gerlach | Manny Rayner | Martin Russell | Helmer Strik

We argue that the field of spoken CALL needs a shared task in order to facilitate comparisons between different groups and methodologies, and describe a concrete example of such a task, based on data collected from a speech-enabled online tool which has been used to help young Swiss German teens practise skills in English conversation. Items are prompt-response pairs, where the prompt is a piece of German text and the response is a recorded English audio file. The task is to label pairs as “accept” or “reject”, accepting responses which are grammatically and linguistically correct to match a set of hidden gold standard answers as closely as possible. Initial resources are provided so that a scratch system can be constructed with a minimal investment of effort, and in particular without necessarily using a speech recogniser. Training data for the task will be released in June 2016, and test data in January 2017.

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Joining-in-type Humanoid Robot Assisted Language Learning System
AlBara Khalifa | Tsuneo Kato | Seiichi Yamamoto

Dialogue robots are attractive to people, and in language learning systems, they motivate learners and let them practice conversational skills in more realistic environment. However, automatic speech recognition (ASR) of the second language (L2) learners is still a challenge, because their speech contains not just pronouncing, lexical, grammatical errors, but is sometimes totally disordered. Hence, we propose a novel robot assisted language learning (RALL) system using two robots, one as a teacher and the other as an advanced learner. The system is designed to simulate multiparty conversation, expecting implicit learning and enhancement of predictability of learners’ utterance through an alignment similar to “interactive alignment”, which is observed in human-human conversation. We collected a database with the prototypes, and measured how much the alignment phenomenon observed in the database with initial analysis.

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OSMAN ― A Novel Arabic Readability Metric
Mahmoud El-Haj | Paul Rayson

We present OSMAN (Open Source Metric for Measuring Arabic Narratives) - a novel open source Arabic readability metric and tool. It allows researchers to calculate readability for Arabic text with and without diacritics. OSMAN is a modified version of the conventional readability formulas such as Flesch and Fog. In our work we introduce a novel approach towards counting short, long and stress syllables in Arabic which is essential for judging readability of Arabic narratives. We also introduce an additional factor called “Faseeh” which considers aspects of script usually dropped in informal Arabic writing. To evaluate our methods we used Spearman’s correlation metric to compare text readability for 73,000 parallel sentences from English and Arabic UN documents. The Arabic sentences were written with the absence of diacritics and in order to count the number of syllables we added the diacritics in using an open source tool called Mishkal. The results show that OSMAN readability formula correlates well with the English ones making it a useful tool for researchers and educators working with Arabic text.

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Evaluating Interactive System Adaptation
Edouard Geoffrois

Enabling users of intelligent systems to enhance the system performance by providing feedback on their errors is an important need. However, the ability of systems to learn from user feedback is difficult to evaluate in an objective and comparative way. Indeed, the involvement of real users in the adaptation process is an impediment to objective evaluation. This issue can be solved by using an oracle approach, where users are simulated by oracles having access to the reference test data. Another difficulty is to find a meaningful metric despite the fact that system improvements depend on the feedback provided and on the system itself. A solution is to measure the minimal amount of information needed to correct all system errors. It can be shown that for any well defined non interactive task, the interactively supervised version of the task can be evaluated by combining such an oracle-based approach and a minimum supervision rate metric. This new evaluation protocol for adaptive systems is not only expected to drive progress for such systems, but also to pave the way for a specialisation of actors along the value chain of their technological development.

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Complementarity, F-score, and NLP Evaluation
Leon Derczynski

This paper addresses the problem of quantifying the differences between entity extraction systems, where in general only a small proportion a document should be selected. Comparing overall accuracy is not very useful in these cases, as small differences in accuracy may correspond to huge differences in selections over the target minority class. Conventionally, one may use per-token complementarity to describe these differences, but it is not very useful when the set is heavily skewed. In such situations, which are common in information retrieval and entity recognition, metrics like precision and recall are typically used to describe performance. However, precision and recall fail to describe the differences between sets of objects selected by different decision strategies, instead just describing the proportional amount of correct and incorrect objects selected. This paper presents a method for measuring complementarity for precision, recall and F-score, quantifying the difference between entity extraction approaches.

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DRANZIERA: An Evaluation Protocol For Multi-Domain Opinion Mining
Mauro Dragoni | Andrea Tettamanzi | Célia da Costa Pereira

Opinion Mining is a topic which attracted a lot of interest in the last years. By observing the literature, it is often hard to replicate system evaluation due to the unavailability of the data used for the evaluation or to the lack of details about the protocol used in the campaign. In this paper, we propose an evaluation protocol, called DRANZIERA, composed of a multi-domain dataset and guidelines allowing both to evaluate opinion mining systems in different contexts (Closed, Semi-Open, and Open) and to compare them to each other and to a number of baselines.

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Evaluating a Topic Modelling Approach to Measuring Corpus Similarity
Richard Fothergill | Paul Cook | Timothy Baldwin

Web corpora are often constructed automatically, and their contents are therefore often not well understood. One technique for assessing the composition of such a web corpus is to empirically measure its similarity to a reference corpus whose composition is known. In this paper we evaluate a number of measures of corpus similarity, including a method based on topic modelling which has not been previously evaluated for this task. To evaluate these methods we use known-similarity corpora that have been previously used for this purpose, as well as a number of newly-constructed known-similarity corpora targeting differences in genre, topic, time, and region. Our findings indicate that, overall, the topic modelling approach did not improve on a chi-square method that had previously been found to work well for measuring corpus similarity.

This contribution presents the background, design and results of a study of users of three oral corpus platforms in Germany. Roughly 5.000 registered users of the Database for Spoken German (DGD), the GeWiss corpus and the corpora of the Hamburg Centre for Language Corpora (HZSK) were asked to participate in a user survey. This quantitative approach was complemented by qualitative interviews with selected users. We briefly introduce the corpus resources involved in the study in section 2. Section 3 describes the methods employed in the user studies. Section 4 summarizes results of the studies focusing on selected key topics. Section 5 attempts a generalization of these results to larger contexts.

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Building a Corpus of Errors and Quality in Machine Translation: Experiments on Error Impact
Ângela Costa | Rui Correia | Luísa Coheur

In this paper we describe a corpus of automatic translations annotated with both error type and quality. The 300 sentences that we have selected were generated by Google Translate, Systran and two in-house Machine Translation systems that use Moses technology. The errors present on the translations were annotated with an error taxonomy that divides errors in five main linguistic categories (Orthography, Lexis, Grammar, Semantics and Discourse), reflecting the language level where the error is located. After the error annotation process, we accessed the translation quality of each sentence using a four point comprehension scale from 1 to 5. Both tasks of error and quality annotation were performed by two different annotators, achieving good levels of inter-annotator agreement. The creation of this corpus allowed us to use it as training data for a translation quality classifier. We concluded on error severity by observing the outputs of two machine learning classifiers: a decision tree and a regression model.

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Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities
Victoria Yaneva | Irina Temnikova | Ruslan Mitkov

This paper presents an approach for automatic evaluation of the readability of text simplification output for readers with cognitive disabilities. First, we present our work towards the development of the EasyRead corpus, which contains easy-to-read documents created especially for people with cognitive disabilities. We then compare the EasyRead corpus to the simplified output contained in the LocalNews corpus (Feng, 2009), the accessibility of which has been evaluated through reading comprehension experiments including 20 adults with mild intellectual disability. This comparison is made on the basis of 13 disability-specific linguistic features. The comparison reveals that there are no major differences between the two corpora, which shows that the EasyRead corpus is to a similar reading level as the user-evaluated texts. We also discuss the role of Simple Wikipedia (Zhu et al., 2010) as a widely-used accessibility benchmark, in light of our finding that it is significantly more complex than both the EasyRead and the LocalNews corpora.

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Word Embedding Evaluation and Combination
Sahar Ghannay | Benoit Favre | Yannick Estève | Nathalie Camelin

Word embeddings have been successfully used in several natural language processing tasks (NLP) and speech processing. Different approaches have been introduced to calculate word embeddings through neural networks. In the literature, many studies focused on word embedding evaluation, but for our knowledge, there are still some gaps. This paper presents a study focusing on a rigorous comparison of the performances of different kinds of word embeddings. These performances are evaluated on different NLP and linguistic tasks, while all the word embeddings are estimated on the same training data using the same vocabulary, the same number of dimensions, and other similar characteristics. The evaluation results reported in this paper match those in the literature, since they point out that the improvements achieved by a word embedding in one task are not consistently observed across all tasks. For that reason, this paper investigates and evaluates approaches to combine word embeddings in order to take advantage of their complementarity, and to look for the effective word embeddings that can achieve good performances on all tasks. As a conclusion, this paper provides new perceptions of intrinsic qualities of the famous word embedding families, which can be different from the ones provided by works previously published in the scientific literature.

In this paper, we claim that the CAMOMILE collaborative annotation platform (developed in the framework of the eponymous CHIST-ERA project) eases the organization of multimedia technology benchmarks, automating most of the campaign technical workflow and enabling collaborative (hence faster and cheaper) annotation of the evaluation data. This is demonstrated through the successful organization of a new multimedia task at MediaEval 2015, Multimodal Person Discovery in Broadcast TV.

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Evaluating the Impact of Light Post-Editing on Usability
Sheila Castilho | Sharon O’Brien

This paper discusses a methodology to measure the usability of machine translated content by end users, comparing lightly post-edited content with raw output and with the usability of source language content. The content selected consists of Online Help articles from a software company for a spreadsheet application, translated from English into German. Three groups of five users each used either the source text - the English version (EN) -, the raw MT version (DE_MT), or the light PE version (DE_PE), and were asked to carry out six tasks. Usability was measured using an eye tracker and cognitive, temporal and pragmatic measures of usability. Satisfaction was measured via a post-task questionnaire presented after the participants had completed the tasks.

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Operational Assessment of Keyword Search on Oral History
Elizabeth Salesky | Jessica Ray | Wade Shen

This project assesses the resources necessary to make oral history searchable by means of automatic speech recognition (ASR). There are many inherent challenges in applying ASR to conversational speech: smaller training set sizes and varying demographics, among others. We assess the impact of dataset size, word error rate and term-weighted value on human search capability through an information retrieval task on Mechanical Turk. We use English oral history data collected by StoryCorps, a national organization that provides all people with the opportunity to record, share and preserve their stories, and control for a variety of demographics including age, gender, birthplace, and dialect on four different training set sizes. We show comparable search performance using a standard speech recognition system as with hand-transcribed data, which is promising for increased accessibility of conversational speech and oral history archives.

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Odin’s Runes: A Rule Language for Information Extraction
Marco A. Valenzuela-Escárcega | Gus Hahn-Powell | Mihai Surdeanu

Odin is an information extraction framework that applies cascades of finite state automata over both surface text and syntactic dependency graphs. Support for syntactic patterns allow us to concisely define relations that are otherwise difficult to express in languages such as Common Pattern Specification Language (CPSL), which are currently limited to shallow linguistic features. The interaction of lexical and syntactic automata provides robustness and flexibility when writing extraction rules. This paper describes Odin’s declarative language for writing these cascaded automata.

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A Classification-based Approach to Economic Event Detection in Dutch News Text
Els Lefever | Véronique Hoste

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.

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Predictive Modeling: Guessing the NLP Terms of Tomorrow
Gil Francopoulo | Joseph Mariani | Patrick Paroubek

Predictive modeling, often called “predictive analytics” in a commercial context, encompasses a variety of statistical techniques that analyze historical and present facts to make predictions about unknown events. Often the unknown events are in the future, but prediction can be applied to any type of unknown whether it be in the past or future. In our case, we present some experiments applying predictive modeling to the usage of technical terms within the NLP domain.

This paper presents the Gavagai Living Lexicon, which is an online distributional semantic model currently available in 20 different languages. We describe the underlying distributional semantic model, and how we have solved some of the challenges in applying such a model to large amounts of streaming data. We also describe the architecture of our implementation, and discuss how we deal with continuous quality assurance of the lexicon.

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Arabic to English Person Name Transliteration using Twitter
Hamdy Mubarak | Ahmed Abdelali

Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data.

Many emerging documents usually contain temporal information. Because the temporal information is useful for various applications, it became important to develop a system of extracting the temporal information from the documents. Before developing the system, it first necessary to define or design the structure of temporal information. In other words, it is necessary to design a language which defines how to annotate the temporal information. There have been some studies about the annotation languages, but most of them was applicable to only a specific target language (e.g., English). Thus, it is necessary to design an individual annotation language for each language. In this paper, we propose a revised version of Koreain Time Mark-up Language (K-TimeML), and also introduce a dataset, named Korean TimeBank, that is constructed basd on the K-TimeML. We believe that the new K-TimeML and Korean TimeBank will be used in many further researches about extraction of temporal information.

Hypernymy relations (those where an hyponym term shares a “isa” relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store them effectively into a large database. This collection of relations represents a rich source of knowledge and may be useful for many researchers. We offer the tuple dataset for public download and an Application Programming Interface (API) to help other researchers programmatically query the database.

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Using a Cross-Language Information Retrieval System based on OHSUMED to Evaluate the Moses and KantanMT Statistical Machine Translation Systems
Nikolaos Katris | Richard Sutcliffe | Theodore Kalamboukis

The objective of this paper was to evaluate the performance of two statistical machine translation (SMT) systems within a cross-language information retrieval (CLIR) architecture and examine if there is a correlation between translation quality and CLIR performance. The SMT systems were KantanMT, a cloud-based machine translation (MT) platform, and Moses, an open-source MT application. First we trained both systems using the same language resources: the EMEA corpus for the translation model and language model and the QTLP corpus for tuning. Then we translated the 63 queries of the OHSUMED test collection from Greek into English using both MT systems. Next, we ran the queries on the document collection using Apache Solr to get a list of the top ten matches. The results were compared to the OHSUMED gold standard. KantanMT achieved higher average precision and F-measure than Moses, while both systems produced the same recall score. We also calculated the BLEU score for each system using the ECDC corpus. Moses achieved a higher BLEU score than KantanMT. Finally, we also tested the IR performance of the original English queries. This work overall showed that CLIR performance can be better even when BLEU score is worse.

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Two Decades of Terminology: European Framework Programmes Titles
Gabriella Pardelli | Sara Goggi | Silvia Giannini | Stefania Biagioni

This work analyses a corpus made of the titles of research projects belonging to the last four European Commission Framework Programmes (FP4, FP5, FP6, FP7) during a time span of nearly two decades (1994-2012). The starting point is the idea of creating a corpus of titles which would constitute a terminological niche, a sort of “cluster map” offering an overall vision on the terms used and the links between them. Moreover, by performing a terminological comparison over a period of time it is possible to trace the presence of obsolete words in outdated research areas as well as of neologisms in the most recent fields. Within this scenario, the minimal purpose is to build a corpus of titles of European projects belonging to the several Framework Programmes in order to obtain a terminological mapping of relevant words in the various research areas: particularly significant would be those terms spread across different domains or those extremely tied to a specific domain. A term could actually be found in many fields and being able to acknowledge and retrieve this cross-presence means being able to linking those different domains by means of a process of terminological mapping.

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Legal Text Interpretation: Identifying Hohfeldian Relations from Text
Wim Peters | Adam Wyner

The paper investigates the extent of the support semi-automatic analysis can provide for the specific task of assigning Hohfeldian relations of Duty, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and the bearers of associated roles. The outcome of the analysis supports scholars in identifying Hohfeldian structures in legal text when performing close reading of the texts. A cyclic workflow involving automated annotation and expert feedback will incrementally increase the quality and coverage of the automatic extraction process, and increasingly reduce the amount of manual work required of the scholar.

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Analysis of English Spelling Errors in a Word-Typing Game
Ryuichi Tachibana | Mamoru Komachi

The emergence of the web has necessitated the need to detect and correct noisy consumer-generated texts. Most of the previous studies on English spelling-error extraction collected English spelling errors from web services such as Twitter by using the edit distance or from input logs utilizing crowdsourcing. However, in the former approach, it is not clear which word corresponds to the spelling error, and the latter approach requires an annotation cost for the crowdsourcing. One notable exception is Rodrigues and Rytting (2012), who proposed to extract English spelling errors by using a word-typing game. Their approach saves the cost of crowdsourcing, and guarantees an exact alignment between the word and the spelling error. However, they did not assert whether the extracted spelling error corpora reflect the usual writing process such as writing a document. Therefore, we propose a new correctable word-typing game that is more similar to the actual writing process. Experimental results showed that we can regard typing-game logs as a source of spelling errors.

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Finding Definitions in Large Corpora with Sketch Engine
Vojtěch Kovář | Monika Močiariková | Pavel Rychlý

The paper describes automatic definition finding implemented within the leading corpus query and management tool, Sketch Engine. The implementation exploits complex pattern-matching queries in the corpus query language (CQL) and the indexing mechanism of word sketches for finding and storing definition candidates throughout the corpus. The approach is evaluated for Czech and English corpora, showing that the results are usable in practice: precision of the tool ranges between 30 and 75 percent (depending on the major corpus text types) and we were able to extract nearly 2 million definition candidates from an English corpus with 1.4 billion words. The feature is embedded into the interface as a concordance filter, so that users can search for definitions of any query to the corpus, including very specific multi-word queries. The results also indicate that ordinary texts (unlike explanatory texts) contain rather low number of definitions, which is perhaps the most important problem with automatic definition finding in general.

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Improving Information Extraction from Wikipedia Texts using Basic English
Teresa Rodríguez-Ferreira | Adrián Rabadán | Raquel Hervás | Alberto Díaz

The aim of this paper is to study the effect that the use of Basic English versus common English has on information extraction from online resources. The amount of online information available to the public grows exponentially, and is potentially an excellent resource for information extraction. The problem is that this information often comes in an unstructured format, such as plain text. In order to retrieve knowledge from this type of text, it must first be analysed to find the relevant details, and the nature of the language used can greatly impact the quality of the extracted information. In this paper, we compare triplets that represent definitions or properties of concepts obtained from three online collaborative resources (English Wikipedia, Simple English Wikipedia and Simple English Wiktionary) and study the differences in the results when Basic English is used instead of common English. The results show that resources written in Basic English produce less quantity of triplets, but with higher quality.

In this paper we present PIERINO (PIattaforma per l’Estrazione e il Recupero di INformazione Online), a system that was implemented in collaboration with the Italian Ministry of Education, University and Research to analyse the citizens’ comments given in #labuonascuola survey. The platform includes various levels of automatic analysis such as key-concept extraction and word co-occurrences. Each analysis is displayed through an intuitive view using different types of visualizations, for example radar charts and sunburst. PIERINO was effectively used to support shaping the last Italian school reform, proving the potential of NLP in the context of policy making.

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Evaluating Translation Quality and CLIR Performance of Query Sessions
Xabier Saralegi | Eneko Agirre | Iñaki Alegria

This paper presents the evaluation of the translation quality and Cross-Lingual Information Retrieval (CLIR) performance when using session information as the context of queries. The hypothesis is that previous queries provide context that helps to solve ambiguous translations in the current query. We tested several strategies on the TREC 2010 Session track dataset, which includes query reformulations grouped by generalization, specification, and drifting types. We study the Basque to English direction, evaluating both the translation quality and CLIR performance, with positive results in both cases. The results show that the quality of translation improved, reducing error rate by 12% (HTER) when using session information, which improved CLIR results 5% (nDCG). We also provide an analysis of the improvements across the three kinds of sessions: generalization, specification, and drifting. Translation quality improved in all three types (generalization, specification, and drifting), and CLIR improved for generalization and specification sessions, preserving the performance in drifting sessions.

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Construction and Analysis of a Large Vietnamese Text Corpus
Dieu-Thu Le | Uwe Quasthoff

This paper presents a new Vietnamese text corpus which contains around 4.05 billion words. It is a collection of Wikipedia texts, newspaper articles and random web texts. The paper describes the process of collecting, cleaning and creating the corpus. Processing Vietnamese texts faced several challenges, for example, different from many Latin languages, Vietnamese language does not use blanks for separating words, hence using common tokenizers such as replacing blanks with word boundary does not work. A short review about different approaches of Vietnamese tokenization is presented together with how the corpus has been processed and created. After that, some statistical analysis on this data is reported including the number of syllable, average word length, sentence length and topic analysis. The corpus is integrated into a framework which allows searching and browsing. Using this web interface, users can find out how many times a particular word appears in the corpus, sample sentences where this word occurs, its left and right neighbors.

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Forecasting Emerging Trends from Scientific Literature
Kartik Asooja | Georgeta Bordea | Gabriela Vulcu | Paul Buitelaar

Text analysis methods for the automatic identification of emerging technologies by analyzing the scientific publications, are gaining attention because of their socio-economic impact. The approaches so far have been mainly focused on retrospective analysis by mapping scientific topic evolution over time. We propose regression based approaches to predict future keyword distribution. The prediction is based on historical data of the keywords, which in our case, are LREC conference proceedings. Considering the insufficient number of data points available from LREC proceedings, we do not employ standard time series forecasting methods. We form a dataset by extracting the keywords from previous year proceedings and quantify their yearly relevance using tf-idf scores. This dataset additionally contains ranked lists of related keywords and experts for each keyword.

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Extracting Structured Scholarly Information from the Machine Translation Literature
Eunsol Choi | Matic Horvat | Jonathan May | Kevin Knight | Daniel Marcu

Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work. We introduce a structured, queryable representation for experimental results and a baseline system that automatically populates this representation. The representation can answer compositional questions such as: “Which are the best published results reported on the NIST 09 Chinese to English dataset?” and “What are the most important methods for speeding up phrase-based decoding?” Answering such questions usually involves lengthy literature surveys. Current machine reading for academic papers does not usually consider the actual experiments, but mostly focuses on understanding abstracts. We describe annotation work to create an initial hscientific paper; experimental results representationi corpus. The corpus is composed of 67 papers which were manually annotated with a structured representation of experimental results by domain experts. Additionally, we present a baseline algorithm that characterizes the difficulty of the inference task.

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Staggered NLP-assisted refinement for Clinical Annotations of Chronic Disease Events
Stephen Wu | Chung-Il Wi | Sunghwan Sohn | Hongfang Liu | Young Juhn

Domain-specific annotations for NLP are often centered on real-world applications of text, and incorrect annotations may be particularly unacceptable. In medical text, the process of manual chart review (of a patient’s medical record) is error-prone due to its complexity. We propose a staggered NLP-assisted approach to the refinement of clinical annotations, an interactive process that allows initial human judgments to be verified or falsified by means of comparison with an improving NLP system. We show on our internal Asthma Timelines dataset that this approach improves the quality of the human-produced clinical annotations.

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“Who was Pietro Badoglio?” Towards a QA system for Italian History
Stefano Menini | Rachele Sprugnoli | Antonio Uva

This paper presents QUANDHO (QUestion ANswering Data for italian HistOry), an Italian question answering dataset created to cover a specific domain, i.e. the history of Italy in the first half of the XX century. The dataset includes questions manually classified and annotated with Lexical Answer Types, and a set of question-answer pairs. This resource, freely available for research purposes, has been used to retrain a domain independent question answering system so to improve its performances in the domain of interest. Ongoing experiments on the development of a question classifier and an automatic tagger of Lexical Answer Types are also presented.

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A Document Repository for Social Media and Speech Conversations
Adam Funk | Robert Gaizauskas | Benoit Favre

We present a successfully implemented document repository REST service for flexible SCRUD (search, crate, read, update, delete) storage of social media conversations, using a GATE/TIPSTER-like document object model and providing a query language for document features. This software is currently being used in the SENSEI research project and will be published as open-source software before the project ends. It is, to the best of our knowledge, the first freely available, general purpose data repository to support large-scale multimodal (i.e., speech or text) conversation analytics.

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Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse
Artemis Parvizi | Matt Kohl | Meritxell Gonzàlez | Roser Saurí

The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chief of which was the capability to losslessly capture diverse features of many different languages in a dictionary format, while supplying a framework for inferring relations like translation, derivation, etc., between the data. Building on valuable features of existing models, and working with OUP monolingual and bilingual dictionary datasets, we have designed and implemented a new linguistic ontology. The ontology has been reviewed by a number of computational linguists, and we are working to move more dictionary data into it. We have also developed APIs to surface the linked data to dictionary websites.

Language resources (LR) are indispensable for the development of tools for machine translation (MT) or various kinds of computer-assisted translation (CAT). In particular language corpora, both parallel and monolingual are considered most important for instance for MT, not only SMT but also hybrid MT. The Language Technology Observatory will provide easy access to information about LRs deemed to be useful for MT and other translation tools through its LR Catalogue. In order to determine what aspects of an LR are useful for MT practitioners, a user study was made, providing a guide to the most relevant metadata and the most relevant quality criteria. We have seen that many resources exist which are useful for MT and similar work, but the majority are for (academic) research or educational use only, and as such not available for commercial use. Our work has revealed a list of gaps: coverage gap, awareness gap, quality gap, quantity gap. The paper ends with recommendations for a forward-looking strategy.

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The Language Application Grid and Galaxy
Nancy Ide | Keith Suderman | James Pustejovsky | Marc Verhagen | Christopher Cieri

The NSF-SI2-funded LAPPS Grid project is a collaborative effort among Brandeis University, Vassar College, Carnegie-Mellon University (CMU), and the Linguistic Data Consortium (LDC), which has developed an open, web-based infrastructure through which resources can be easily accessed and within which tailored language services can be efficiently composed, evaluated, disseminated and consumed by researchers, developers, and students across a wide variety of disciplines. The LAPPS Grid project recently adopted Galaxy (Giardine et al., 2005), a robust, well-developed, and well-supported front end for workflow configuration, management, and persistence. Galaxy allows data inputs and processing steps to be selected from graphical menus, and results are displayed in intuitive plots and summaries that encourage interactive workflows and the exploration of hypotheses. The Galaxy workflow engine provides significant advantages for deploying pipelines of LAPPS Grid web services, including not only means to create and deploy locally-run and even customized versions of the LAPPS Grid as well as running the LAPPS Grid in the cloud, but also access to a huge array of statistical and visualization tools that have been developed for use in genomics research.

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ELRA Activities and Services
Khalid Choukri | Valérie Mapelli | Hélène Mazo | Vladimir Popescu

After celebrating its 20th anniversary in 2015, ELRA is carrying on its strong involvement in the HLT field. To share ELRA’s expertise of those 21 past years, this article begins with a presentation of ELRA’s strategic Data and LR Management Plan for a wide use by the language communities. Then, we further report on ELRA’s activities and services provided since LREC 2014. When looking at the cataloguing and licensing activities, we can see that ELRA has been active at making the Meta-Share repository move toward new developments steps, supporting Europe to obtain accurate LRs within the Connecting Europe Facility programme, promoting the use of LR citation, creating the ELRA License Wizard web portal. The article further elaborates on the recent LR production activities of various written, speech and video resources, commissioned by public and private customers. In parallel, ELDA has also worked on several EU-funded projects centred on strategic issues related to the European Digital Single Market. The last part gives an overview of the latest dissemination activities, with a special focus on the celebration of its 20th anniversary organised in Dubrovnik (Croatia) and the following up of LREC, as well as the launching of the new ELRA portal.

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Mirroring Facial Expressions and Emotions in Dyadic Conversations
Costanza Navarretta

This paper presents an investigation of mirroring facial expressions and the emotions which they convey in dyadic naturally occurring first encounters. Mirroring facial expressions are a common phenomenon in face-to-face interactions, and they are due to the mirror neuron system which has been found in both animals and humans. Researchers have proposed that the mirror neuron system is an important component behind many cognitive processes such as action learning and understanding the emotions of others. Preceding studies of the first encounters have shown that overlapping speech and overlapping facial expressions are very frequent. In this study, we want to determine whether the overlapping facial expressions are mirrored or are otherwise correlated in the encounters, and to what extent mirroring facial expressions convey the same emotion. The results of our study show that the majority of smiles and laughs, and one fifth of the occurrences of raised eyebrows are mirrored in the data. Moreover some facial traits in co-occurring expressions co-occur more often than it would be expected by chance. Finally, amusement, and to a lesser extent friendliness, are often emotions shared by both participants, while other emotions indicating individual affective states such as uncertainty and hesitancy are never showed by both participants, but co-occur with complementary emotions such as friendliness and support. Whether these tendencies are specific to this type of conversations or are more common should be investigated further.

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.

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A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults
Victoria Yaneva | Irina Temnikova | Ruslan Mitkov

The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers. The data was elicited through reading comprehension testing combined with eye-tracking recording. The corpus consists of 1034 content words tagged with their POS, syntactic role and three gaze-based measures corresponding to the autistic and control participants. The reading skills of the participants were measured through multiple-choice questions and, based on the answers given, they were divided into groups of skillful and less-skillful readers. This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability. In addition to describing the process of data collection and corpus development, we present a study on the effect that word length has on reading in autism. The corpus is intended as a resource for investigating the particular linguistic constructions which pose reading difficulties for people with autism and hopefully, as a way to inform future text simplification research intended for this population.

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A Multimodal Corpus for the Assessment of Public Speaking Ability and Anxiety
Mathieu Chollet | Torsten Wörtwein | Louis-Philippe Morency | Stefan Scherer

The ability to efficiently speak in public is an essential asset for many professions and is used in everyday life. As such, tools enabling the improvement of public speaking performance and the assessment and mitigation of anxiety related to public speaking would be very useful. Multimodal interaction technologies, such as computer vision and embodied conversational agents, have recently been investigated for the training and assessment of interpersonal skills. Once central requirement for these technologies is multimodal corpora for training machine learning models. This paper addresses the need of these technologies by presenting and sharing a multimodal corpus of public speaking presentations. These presentations were collected in an experimental study investigating the potential of interactive virtual audiences for public speaking training. This corpus includes audio-visual data and automatically extracted features, measures of public speaking anxiety and personality, annotations of participants’ behaviors and expert ratings of behavioral aspects and overall performance of the presenters. We hope this corpus will help other research teams in developing tools for supporting public speaking training.

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Deep Learning of Audio and Language Features for Humor Prediction
Dario Bertero | Pascale Fung

We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: “The Big Bang Theory”. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5% over 66.5% by CRF and 52.9% by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.

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An Empirical Study of Arabic Formulaic Sequence Extraction Methods
Ayman Alghamdi | Eric Atwell | Claire Brierley

This paper aims to implement what is referred to as the collocation of the Arabic keywords approach for extracting formulaic sequences (FSs) in the form of high frequency but semantically regular formulas that are not restricted to any syntactic construction or semantic domain. The study applies several distributional semantic models in order to automatically extract relevant FSs related to Arabic keywords. The data sets used in this experiment are rendered from a new developed corpus-based Arabic wordlist consisting of 5,189 lexical items which represent a variety of modern standard Arabic (MSA) genres and regions, the new wordlist being based on an overlapping frequency based on a comprehensive comparison of four large Arabic corpora with a total size of over 8 billion running words. Empirical n-best precision evaluation methods are used to determine the best association measures (AMs) for extracting high frequency and meaningful FSs. The gold standard reference FSs list was developed in previous studies and manually evaluated against well-established quantitative and qualitative criteria. The results demonstrate that the MI.log_f AM achieved the highest results in extracting significant FSs from the large MSA corpus, while the T-score association measure achieved the worst results.

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Rule-based Automatic Multi-word Term Extraction and Lemmatization
Ranka Stanković | Cvetana Krstev | Ivan Obradović | Biljana Lazić | Aleksandra Trtovac

In this paper we present a rule-based method for multi-word term extraction that relies on extensive lexical resources in the form of electronic dictionaries and finite-state transducers for modelling various syntactic structures of multi-word terms. The same technology is used for lemmatization of extracted multi-word terms, which is unavoidable for highly inflected languages in order to pass extracted data to evaluators and subsequently to terminological e-dictionaries and databases. The approach is illustrated on a corpus of Serbian texts from the mining domain containing more than 600,000 simple word forms. Extracted and lemmatized multi-word terms are filtered in order to reject falsely offered lemmas and then ranked by introducing measures that combine linguistic and statistical information (C-Value, T-Score, LLR, and Keyness). Mean average precision for retrieval of MWU forms ranges from 0.789 to 0.804, while mean average precision of lemma production ranges from 0.956 to 0.960. The evaluation showed that 94% of distinct multi-word forms were evaluated as proper multi-word units, and among them 97% were associated with correct lemmas.

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Distribution of Valency Complements in Czech Complex Predicates: Between Verb and Noun
Václava Kettnerová | Eduard Bejček

In this paper, we focus on Czech complex predicates formed by a light verb and a predicative noun expressed as the direct object. Although Czech ― as an inflectional language encoding syntactic relations via morphological cases ― provides an excellent opportunity to study the distribution of valency complements in the syntactic structure with complex predicates, this distribution has not been described so far. On the basis of a manual analysis of the richly annotated data from the Prague Dependency Treebank, we thus formulate principles governing this distribution. In an automatic experiment, we verify these principles on well-formed syntactic structures from the Prague Dependency Treebank and the Prague Czech-English Dependency Treebank with very satisfactory results: the distribution of 97% of valency complements in the surface structure is governed by the proposed principles. These results corroborate that the surface structure formation of complex predicates is a regular process.

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A Lexical Resource of Hebrew Verb-Noun Multi-Word Expressions
Chaya Liebeskind | Yaakov HaCohen-Kerner

A verb-noun Multi-Word Expression (MWE) is a combination of a verb and a noun with or without other words, in which the combination has a meaning different from the meaning of the words considered separately. In this paper, we present a new lexical resource of Hebrew Verb-Noun MWEs (VN-MWEs). The VN-MWEs of this resource were manually collected and annotated from five different web resources. In addition, we analyze the lexical properties of Hebrew VN-MWEs by classifying them to three types: morphological, syntactic, and semantic. These two contributions are essential for designing algorithms for automatic VN-MWEs extraction. The analysis suggests some interesting features of VN-MWEs for exploration. The lexical resource enables to sample a set of positive examples for Hebrew VN-MWEs. This set of examples can either be used for training supervised algorithms or as seeds in unsupervised bootstrapping algorithms. Thus, this resource is a first step towards automatic identification of Hebrew VN-MWEs, which is important for natural language understanding, generation and translation systems.

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Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
Guillaume Jacquet | Maud Ehrmann | Ralf Steinberger | Jaakko Väyrynen

This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource. Starting from a collection of millions of acronym/expansion pairs for 22 languages where expansion variants were grouped into monolingual clusters, we experiment with several aggregation strategies to link these clusters across languages. Aggregation strategies make use of string similarity distances and translation probabilities and they are based on vector space and graph representations. The accuracy of the approach is evaluated against Wikipedia’s redirection and cross-lingual linking tables. The resulting multi-word entity resource contains 64,000 multi-word entities with unique identifiers and their 600,000 multilingual lexical variants. We intend to make this new resource publicly available.

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SemLinker, a Modular and Open Source Framework for Named Entity Discovery and Linking
Marie-Jean Meurs | Hayda Almeida | Ludovic Jean-Louis | Eric Charton

This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically. SemLinker relies on several modules that perform surface form generation, mutual disambiguation, entity clustering, and make use of two annotation engines. SemLinker was evaluated in the English Entity Discovery and Linking track of the Text Analysis Conference on Knowledge Base Population, organized by the US National Institute of Standards and Technology. Along with the SemLinker source code, we release our annotation files containing the discovered named entities, their types, and position across processed documents.

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Context-enhanced Adaptive Entity Linking
Filip Ilievski | Giuseppe Rizzo | Marieke van Erp | Julien Plu | Raphaël Troncy

More and more knowledge bases are publicly available as linked data. Since these knowledge bases contain structured descriptions of real-world entities, they can be exploited by entity linking systems that anchor entity mentions from text to the most relevant resources describing those entities. In this paper, we investigate adaptation of the entity linking task using contextual knowledge. The key intuition is that entity linking can be customized depending on the textual content, as well as on the application that would make use of the extracted information. We present an adaptive approach that relies on contextual knowledge from text to enhance the performance of ADEL, a hybrid linguistic and graph-based entity linking system. We evaluate our approach on a domain-specific corpus consisting of annotated WikiNews articles.

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Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
Eda Okur | Hakan Demir | Arzucan Özgür

Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised method for learning continuous representations of words in vector space. We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts. We evaluated our Turkish NER system on Twitter messages and achieved better F-score performances than the published results of previously proposed NER systems on Turkish tweets. Since we did not employ any language dependent features, we believe that our method can be easily adapted to microblog texts in other morphologically rich languages.

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Entity Linking with a Paraphrase Flavor
Maria Pershina | Yifan He | Ralph Grishman

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5% in B3+F clustering score on diagnostic TAC EDL 2014 data.

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Domain Adaptation for Named Entity Recognition Using CRFs
Tian Tian | Marco Dinarelli | Isabelle Tellier | Pedro Dias Cardoso

In this paper we explain how we created a labelled corpus in English for a Named Entity Recognition (NER) task from multi-source and multi-domain data, for an industrial partner. We explain the specificities of this corpus with examples and describe some baseline experiments. We present some results of domain adaptation on this corpus using a labelled Twitter corpus (Ritter et al., 2011). We tested a semi-supervised method from (Garcia-Fernandez et al., 2014) combined with a supervised domain adaptation approach proposed in (Raymond and Fayolle, 2010) for machine learning experiments with CRFs (Conditional Random Fields). We use the same technique to improve the NER results on the Twitter corpus (Ritter et al., 2011). Our contributions thus consist in an industrial corpus creation and NER performance improvements.

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IRIS: English-Irish Machine Translation System
Mihael Arcan | Caoilfhionn Lane | Eoin Ó Droighneáin | Paul Buitelaar

We describe IRIS, a statistical machine translation (SMT) system for translating from English into Irish and vice versa. Since Irish is considered an under-resourced language with a limited amount of machine-readable text, building a machine translation system that produces reasonable translations is rather challenging. As translation is a difficult task, current research in SMT focuses on obtaining statistics either from a large amount of parallel, monolingual or other multilingual resources. Nevertheless, we collected available English-Irish data and developed an SMT system aimed at supporting human translators and enabling cross-lingual language technology tasks.

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Linguistically Inspired Language Model Augmentation for MT
George Tambouratzis | Vasiliki Pouli

The present article reports on efforts to improve the translation accuracy of a corpus―based Machine Translation (MT) system. In order to achieve that, an error analysis performed on past translation outputs has indicated the likelihood of improving the translation accuracy by augmenting the coverage of the Target-Language (TL) side language model. The method adopted for improving the language model is initially presented, based on the concatenation of consecutive phrases. The algorithmic steps are then described that form the process for augmenting the language model. The key idea is to only augment the language model to cover the most frequent cases of phrase sequences, as counted over a TL-side corpus, in order to maximize the cases covered by the new language model entries. Experiments presented in the article show that substantial improvements in translation accuracy are achieved via the proposed method, when integrating the grown language model to the corpus-based MT system.

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A Rule-based Shallow-transfer Machine Translation System for Scots and English
Gavin Abercrombie

An open-source rule-based machine translation system is developed for Scots, a low-resourced minor language closely related to English and spoken in Scotland and Ireland. By concentrating on translation for assimilation (gist comprehension) from Scots to English, it is proposed that the development of dictionaries designed to be used with in the Apertium platform will be sufficient to produce translations that improve non-Scots speakers understanding of the language. Mono- and bilingual Scots dictionaries are constructed using lexical items gathered from a variety of resources across several domains. Although the primary goal of this project is translation for gisting, the system is evaluated for both assimilation and dissemination (publication-ready translations). A variety of evaluation methods are used, including a cloze test undertaken by human volunteers. While evaluation results are comparable to, and in some cases superior to, those of other language pairs within the Apertium platform, room for improvement is identified in several areas of the system.

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Syntax-based Multi-system Machine Translation
Matīss Rikters | Inguna Skadiņa

This paper describes a hybrid machine translation system that explores a parser to acquire syntactic chunks of a source sentence, translates the chunks with multiple online machine translation (MT) system application program interfaces (APIs) and creates output by combining translated chunks to obtain the best possible translation. The selection of the best translation hypothesis is performed by calculating the perplexity for each translated chunk. The goal of this approach is to enhance the baseline multi-system hybrid translation (MHyT) system that uses only a language model to select best translation from translations obtained with different APIs and to improve overall English ― Latvian machine translation quality over each of the individual MT APIs. The presented syntax-based multi-system translation (SyMHyT) system demonstrates an improvement in terms of BLEU and NIST scores compared to the baseline system. Improvements reach from 1.74 up to 2.54 BLEU points.

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Use of Domain-Specific Language Resources in Machine Translation
Sanja Štajner | Andreia Querido | Nuno Rendeiro | João António Rodrigues | António Branco

In this paper, we address the problem of Machine Translation (MT) for a specialised domain in a language pair for which only a very small domain-specific parallel corpus is available. We conduct a series of experiments using a purely phrase-based SMT (PBSMT) system and a hybrid MT system (TectoMT), testing three different strategies to overcome the problem of the small amount of in-domain training data. Our results show that adding a small size in-domain bilingual terminology to the small in-domain training corpus leads to the best improvements of a hybrid MT system, while the PBSMT system achieves the best results by adding a combination of in-domain bilingual terminology and a larger out-of-domain corpus. We focus on qualitative human evaluation of the output of two best systems (one for each approach) and perform a systematic in-depth error analysis which revealed advantages of the hybrid MT system over the pure PBSMT system for this specific task.

This paper presents CATaLog online, a new web-based MT and TM post-editing tool. CATaLog online is a freeware software that can be used through a web browser and it requires only a simple registration. The tool features a number of editing and log functions similar to the desktop version of CATaLog enhanced with several new features that we describe in detail in this paper. CATaLog online is designed to allow users to post-edit both translation memory segments as well as machine translation output. The tool provides a complete set of log information currently not available in most commercial CAT tools. Log information can be used both for project management purposes as well as for the study of the translation process and translator’s productivity.

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The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus
Akira Hayakawa | Saturnino Luz | Loredana Cerrato | Nick Campbell

This paper presents the multimodal Interlingual Map Task Corpus (ILMT-s2s corpus) collected at Trinity College Dublin, and discuss some of the issues related to the collection and analysis of the data. The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour. The simplicity of the task, and the complexity of phenomena it can elicit, make the map task an ideal object of study. Although there are studies that used replications of the map task to investigate communication in computer mediated tasks, this ILMT-s2s corpus is, to the best of our knowledge, the first investigation of communicative behaviour in the presence of three additional “filters”: Automatic Speech Recognition (ASR), Machine Translation (MT) and Text To Speech (TTS) synthesis, where the instruction giver and the instruction follower speak different languages. This paper details the data collection setup and completed annotation of the ILMT-s2s corpus, and outlines preliminary results obtained from the data.

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Name Translation based on Fine-grained Named Entity Recognition in a Single Language
Kugatsu Sadamitsu | Itsumi Saito | Taichi Katayama | Hisako Asano | Yoshihiro Matsuo

We propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT). The methods are based on a bilingual named entity recognizer that uses a monolingual named entity recognizer with transliteration. Through experiments, we demonstrate that incorporating fine-grained named entities into statistical machine translation improves the accuracy of SMT with more adequate granularity compared with the standard SMT, which is a non-named entity abstraction method.

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Lexical Resources to Enrich English Malayalam Machine Translation
Sreelekha S | Pushpak Bhattacharyya

In this paper we present our work on the usage of lexical resources for the Machine Translation English and Malayalam. We describe a comparative performance between different Statistical Machine Translation (SMT) systems on top of phrase based SMT system as baseline. We explore different ways of utilizing lexical resources to improve the quality of English Malayalam statistical machine translation. In order to enrich the training corpus we have augmented the lexical resources in two ways (a) additional vocabulary and (b) inflected verbal forms. Lexical resources include IndoWordnet semantic relation set, lexical words and verb phrases etc. We have described case studies, evaluations and have given detailed error analysis for both Malayalam to English and English to Malayalam machine translation systems. We observed significant improvement in evaluations of translation quality. Lexical resources do help uplift performance when parallel corpora are scanty.

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Novel elicitation and annotation schemes for sentential and sub-sentential alignments of bitexts
Yong Xu | François Yvon

Resources for evaluating sentence-level and word-level alignment algorithms are unsatisfactory. Regarding sentence alignments, the existing data is too scarce, especially when it comes to difficult bitexts, containing instances of non-literal translations. Regarding word-level alignments, most available hand-aligned data provide a complete annotation at the level of words that is difficult to exploit, for lack of a clear semantics for alignment links. In this study, we propose new methodologies for collecting human judgements on alignment links, which have been used to annotate 4 new data sets, at the sentence and at the word level. These will be released online, with the hope that they will prove useful to evaluate alignment software and quality estimation tools for automatic alignment. Keywords: Parallel corpora, Sentence Alignments, Word Alignments, Confidence Estimation

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PROTEST: A Test Suite for Evaluating Pronouns in Machine Translation
Liane Guillou | Christian Hardmeier

We present PROTEST, a test suite for the evaluation of pronoun translation by MT systems. The test suite comprises 250 hand-selected pronoun tokens and an automatic evaluation method which compares the translations of pronouns in MT output with those in the reference translation. Pronoun translations that do not match the reference are referred for manual evaluation. PROTEST is designed to support analysis of system performance at the level of individual pronoun groups, rather than to provide a single aggregate measure over all pronouns. We wish to encourage detailed analyses to highlight issues in the handling of specific linguistic mechanisms by MT systems, thereby contributing to a better understanding of those problems involved in translating pronouns. We present two use cases for PROTEST: a) for measuring improvement/degradation of an incremental system change, and b) for comparing the performance of a group of systems whose design may be largely unrelated. Following the latter use case, we demonstrate the application of PROTEST to the evaluation of the systems submitted to the DiscoMT 2015 shared task on pronoun translation.

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Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation
Chenhui Chu | Sadao Kurohashi

Out-of-vocabulary (OOV) word is a crucial problem in statistical machine translation (SMT) with low resources. OOV paraphrasing that augments the translation model for the OOV words by using the translation knowledge of their paraphrases has been proposed to address the OOV problem. In this paper, we propose using word embeddings and semantic lexicons for OOV paraphrasing. Experiments conducted on a low resource setting of the OLYMPICS task of IWSLT 2012 verify the effectiveness of our proposed method.

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The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions
Joachim Daiber | Rob van der Goot

We introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets. This benchmark enables the evaluation of parser robustness as well as text normalization methods, including normalization as machine translation and unsupervised lexical normalization, directly on syntactic trees. Experiments show that text normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy on this test set.

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Punctuation Prediction for Unsegmented Transcript Based on Word Vector
Xiaoyin Che | Cheng Wang | Haojin Yang | Christoph Meinel

In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.

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Evaluating a Deterministic Shift-Reduce Neural Parser for Constituent Parsing
Hao Zhou | Yue Zhang | Shujian Huang | Xin-Yu Dai | Jiajun Chen

Greedy transition-based parsers are appealing for their very fast speed, with reasonably high accuracies. In this paper, we build a fast shift-reduce neural constituent parser by using a neural network to make local decisions. One challenge to the parsing speed is the large hidden and output layer sizes caused by the number of constituent labels and branching options. We speed up the parser by using a hierarchical output layer, inspired by the hierarchical log-bilinear neural language model. In standard WSJ experiments, the neural parser achieves an almost 2.4 time speed up (320 sen/sec) compared to a non-hierarchical baseline without significant accuracy loss (89.06 vs 89.13 F-score).

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Language Resource Addition Strategies for Raw Text Parsing
Atsushi Ushiku | Tetsuro Sasada | Shinsuke Mori

We focus on the improvement of accuracy of raw text parsing, from the viewpoint of language resource addition. In Japanese, the raw text parsing is divided into three steps: word segmentation, part-of-speech tagging, and dependency parsing. We investigate the contribution of language resource addition in each of three steps to the improvement in accuracy for two domain corpora. The experimental results show that this improvement depends on the target domain. For example, when we handle well-written texts of limited vocabulary, white paper, an effective language resource is a word-POS pair sequence corpus for the parsing accuracy. So we conclude that it is important to check out the characteristics of the target domain and to choose a suitable language resource addition strategy for the parsing accuracy improvement.

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E-TIPSY: Search Query Corpus Annotated with Entities, Term Importance, POS Tags, and Syntactic Parses
Yuval Marton | Kristina Toutanova

We present E-TIPSY, a search query corpus annotated with named Entities, Term Importance, POS tags, and SYntactic parses. This corpus contains crowdsourced (gold) annotations of the three most important terms in each query. In addition, it contains automatically produced annotations of named entities, part-of-speech tags, and syntactic parses for the same queries. This corpus comes in two formats: (1) Sober Subset: annotations that two or more crowd workers agreed upon, and (2) Full Glass: all annotations. We analyze the strikingly low correlation between term importance and syntactic headedness, which invites research into effective ways of combining these different signals. Our corpus can serve as a benchmark for term importance methods aimed at improving search engine quality and as an initial step toward developing a dataset of gold linguistic analysis of web search queries. In addition, it can be used as a basis for linguistic inquiries into the kind of expressions used in search.

Compared to well-resourced languages such as English and Dutch, natural language processing (NLP) tools for Afrikaans are still not abundant. In the context of the AfriBooms project, KU Leuven and the North-West University collaborated to develop a first, small treebank, a dependency parser, and an easy to use online linguistic search engine for Afrikaans for use by researchers and students in the humanities and social sciences. The search tool is based on a similar development for Dutch, i.e. GrETEL, a user-friendly search engine which allows users to query a treebank by means of a natural language example instead of a formal search instruction.

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Differentia compositionem facit. A Slower-Paced and Reliable Parser for Latin
Edoardo Maria Ponti | Marco Passarotti

The Index Thomisticus Treebank is the largest available treebank for Latin; it contains Medieval Latin texts by Thomas Aquinas. After experimenting on its data with a number of dependency parsers based on different supervised machine learning techniques, we found that DeSR with a multilayer perceptron algorithm, a right-to-left transition, and a tailor-made feature model is the parser providing the highest accuracy rates. We improved the results further by using a technique that combines the output parses of DeSR with those provided by other parsers, outperforming the previous state of the art in parsing the Index Thomisticus Treebank. The key idea behind such improvement is to ensure a sufficient diversity and accuracy of the outputs to be combined; for this reason, we performed an in-depth evaluation of the results provided by the different parsers that we combined. Finally, we assessed that, although the general architecture of the parser is portable to Classical Latin, yet the model trained on Medieval Latin is inadequate for such purpose.

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South African Language Resources: Phrase Chunking
Roald Eiselen

Phrase chunking remains an important natural language processing (NLP) technique for intermediate syntactic processing. This paper describes the development of protocols, annotated phrase chunking data sets and automatic phrase chunkers for ten South African languages. Various problems with adapting the existing annotation protocols of English are discussed as well as an overview of the annotated data sets. Based on the annotated sets, CRF-based phrase chunkers are created and tested with a combination of different features, including part of speech tags and character n-grams. The results of the phrase chunking evaluation show that disjunctively written languages can achieve notably better results for phrase chunking with a limited data set than conjunctive languages, but that the addition of character n-grams improve the results for conjunctive languages.

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Neural Scoring Function for MST Parser
Jindřich Libovický

Continuous word representations appeared to be a useful feature in many natural language processing tasks. Using fixed-dimension pre-trained word embeddings allows avoiding sparse bag-of-words representation and to train models with fewer parameters. In this paper, we use fixed pre-trained word embeddings as additional features for a neural scoring function in the MST parser. With the multi-layer architecture of the scoring function we can avoid handcrafting feature conjunctions. The continuous word representations on the input also allow us to reduce the number of lexical features, make the parser more robust to out-of-vocabulary words, and reduce the total number of parameters of the model. Although its accuracy stays below the state of the art, the model size is substantially smaller than with the standard features set. Moreover, it performs well for languages where only a smaller treebank is available and the results promise to be useful in cross-lingual parsing.

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Analysing Constraint Grammars with a SAT-solver
Inari Listenmaa | Koen Claessen

We describe a method for analysing Constraint Grammars (CG) that can detect internal conflicts and redundancies in a given grammar, without the need for a corpus. The aim is for grammar writers to be able to automatically diagnose, and then manually improve their grammars. Our method works by translating the given grammar into logical constraints that are analysed by a SAT-solver. We have evaluated our analysis on a number of non-trivial grammars and found inconsistencies.

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Old French Dependency Parsing: Results of Two Parsers Analysed from a Linguistic Point of View
Achim Stein

The treatment of medieval texts is a particular challenge for parsers. I compare how two dependency parsers, one graph-based, the other transition-based, perform on Old French, facing some typical problems of medieval texts: graphical variation, relatively free word order, and syntactic variation of several parameters over a diachronic period of about 300 years. Both parsers were trained and evaluated on the “Syntactic Reference Corpus of Medieval French” (SRCMF), a manually annotated dependency treebank. I discuss the relation between types of parsers and types of language, as well as the differences of the analyses from a linguistic point of view.

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Semi-automatic Parsing for Web Knowledge Extraction through Semantic Annotation
Maria Pia di Buono

Parsing Web information, namely parsing content to find relevant documents on the basis of a user’s query, represents a crucial step to guarantee fast and accurate Information Retrieval (IR). Generally, an automated approach to such task is considered faster and cheaper than manual systems. Nevertheless, results do not seem have a high level of accuracy, indeed, as also Hjorland (2007) states, using stochastic algorithms entails: • Low precision due to the indexing of common Atomic Linguistic Units (ALUs) or sentences. • Low recall caused by the presence of synonyms. • Generic results arising from the use of too broad or too narrow terms. Usually IR systems are based on invert text index, namely an index data structure storing a mapping from content to its locations in a database file, or in a document or a set of documents. In this paper we propose a system, by means of which we will develop a search engine able to process online documents, starting from a natural language query, and to return information to users. The proposed approach, based on the Lexicon-Grammar (LG) framework and its language formalization methodologies, aims at integrating a semantic annotation process for both query analysis and document retrieval.

The Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE) was created to facilitate the study of challenges posed by rapidly aging societies in developed countries such as Germany. ILSE contains over 8,000 hours of biographic interviews recorded from more than 1,000 participants over the course of 20 years. Investigations on various aspects of aging, such as cognitive decline, often rely on the analysis of linguistic features which can be derived from spoken content like these interviews. However, transcribing speech is a time and cost consuming manual process and so far only 380 hours of ILSE interviews have been transcribed. Thus, it is the aim of our work to establish technical systems to fully automatically transcribe the ILSE interview data. The joint occurrence of poor recording quality, long audio segments, erroneous transcriptions, varying speaking styles & crosstalk, and emotional & dialectal speech in these interviews presents challenges for automatic speech recognition (ASR). We describe our ongoing work towards the fully automatic transcription of all ILSE interviews and the steps we implemented in preparing the transcriptions to meet the interviews’ challenges. Using a recursive long audio alignment procedure 96 hours of the transcribed data have been made accessible for ASR training.

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FABIOLE, a Speech Database for Forensic Speaker Comparison
Moez Ajili | Jean-François Bonastre | Juliette Kahn | Solange Rossato | Guillaume Bernard

A speech database has been collected for use to highlight the importance of “speaker factor” in forensic voice comparison. FABIOLE has been created during the FABIOLE project funded by the French Research Agency (ANR) from 2013 to 2016. This corpus consists in more than 3 thousands excerpts spoken by 130 French native male speakers. The speakers are divided into two categories: 30 target speakers who everyone has 100 excerpts and 100 “impostors” who everyone has only one excerpt. The data were collected from 10 different French radio and television shows where each utterance turns with a minimum duration of 30s and has a good speech quality. The data set is mainly used for investigating speaker factor in forensic voice comparison and interpreting some unsolved issue such as the relationship between speaker characteristics and system behavior. In this paper, we present FABIOLE database. Then, preliminary experiments are performed to evaluate the effect of the “speaker factor” and the show on a voice comparison system behavior.

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Phonetic Inventory for an Arabic Speech Corpus
Nawar Halabi | Mike Wald

Corpus design for speech synthesis is a well-researched topic in languages such as English compared to Modern Standard Arabic, and there is a tendency to focus on methods to automatically generate the orthographic transcript to be recorded (usually greedy methods). In this work, a study of Modern Standard Arabic (MSA) phonetics and phonology is conducted in order to create criteria for a greedy method to create a speech corpus transcript for recording. The size of the dataset is reduced a number of times using these optimisation methods with different parameters to yield a much smaller dataset with identical phonetic coverage than before the reduction, and this output transcript is chosen for recording. This is part of a larger work to create a completely annotated and segmented speech corpus for MSA.

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AIMU: Actionable Items for Meeting Understanding
Yun-Nung Chen | Dilek Hakkani-Tür

With emerging conversational data, automated content analysis is needed for better data interpretation, so that it is accurately understood and can be effectively integrated and utilized in various applications. ICSI meeting corpus is a publicly released data set of multi-party meetings in an organization that has been released over a decade ago, and has been fostering meeting understanding research since then. The original data collection includes transcription of participant turns as well as meta-data annotations, such as disfluencies and dialog act tags. This paper presents an extended set of annotations for the ICSI meeting corpus with a goal of deeply understanding meeting conversations, where participant turns are annotated by actionable items that could be performed by an automated meeting assistant. In addition to the user utterances that contain an actionable item, annotations also include the arguments associated with the actionable item. The set of actionable items are determined by aligning human-human interactions to human-machine interactions, where a data annotation schema designed for a virtual personal assistant (human-machine genre) is adapted to the meetings domain (human-human genre). The data set is formed by annotating participants’ utterances in meetings with potential intents/actions considering their contexts. The set of actions target what could be accomplished by an automated meeting assistant, such as taking a note of action items that a participant commits to, or finding emails or topic related documents that were mentioned during the meeting. A total of 10 defined intents/actions are considered as actionable items in meetings. Turns that include actionable intents were annotated for 22 public ICSI meetings, that include a total of 21K utterances, segmented by speaker turns. Participants’ spoken turns, possible actions along with associated arguments and their vector representations as computed by convolutional deep structured semantic models are included in the data set for future research. We present a detailed statistical analysis of the data set and analyze the performance of applying convolutional deep structured semantic models for an actionable item detection task. The data is available at http://research.microsoft.com/projects/meetingunderstanding/.

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A Taxonomy of Specific Problem Classes in Text-to-Speech Synthesis: Comparing Commercial and Open Source Performance
Felix Burkhardt | Uwe D. Reichel

Current state-of-the-art speech synthesizers for domain-independent systems still struggle with the challenge of generating understandable and natural-sounding speech. This is mainly because the pronunciation of words of foreign origin, inflections and compound words often cannot be handled by rules. Furthermore there are too many of these for inclusion in exception dictionaries. We describe an approach to evaluating text-to-speech synthesizers with a subjective listening experiment. The focus is to differentiate between known problem classes for speech synthesizers. The target language is German but we believe that many of the described phenomena are not language specific. We distinguish the following problem categories: Normalization, Foreign linguistics, Natural writing, Language specific and General. Each of them is divided into five to three problem classes. Word lists for each of the above mentioned categories were compiled and synthesized by both a commercial and an open source synthesizer, both being based on the non-uniform unit-selection approach. The synthesized speech was evaluated by human judges using the Speechalyzer toolkit and the results are discussed. It shows that, as expected, the commercial synthesizer performs much better than the open-source one, and especially words of foreign origin were pronounced badly by both systems.

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A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems
Patricia Braunger | Hansjörg Hofmann | Steffen Werner | Maria Schmidt

Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech recognition. These methods require a high number of natural language utterances to train the speech recognition engine and to assess the quality of the system. Since human speech offers many variants associated with a single intent, a high number of user utterances have to be elicited. Developers are therefore turning to crowdsourcing to collect this data. This paper compares three different methods to elicit multiple utterances for given semantics via crowd sourcing, namely with pictures, with text and with semantic entities. Specifically, we compare the methods with regard to the number of valid data and linguistic variance, whereby a quantitative and qualitative approach is proposed. In our study, the method with text led to a high variance in the utterances and a relatively low rate of invalid data.

This paper describes the characteristics and structure of a Basque singing voice database of bertsolaritza. Bertsolaritza is a popular singing style from Basque Country sung exclusively in Basque that is improvised and a capella. The database is designed to be used in statistical singing voice synthesis for bertsolaritza style. Starting from the recordings and transcriptions of numerous singers, diarization and phoneme alignment experiments have been made to extract the singing voice from the recordings and create phoneme alignments. This labelling processes have been performed applying standard speech processing techniques and the results prove that these techniques can be used in this specific singing style.

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AMISCO: The Austrian German Multi-Sensor Corpus
Hannes Pessentheiner | Thomas Pichler | Martin Hagmüller

We introduce a unique, comprehensive Austrian German multi-sensor corpus with moving and non-moving speakers to facilitate the evaluation of estimators and detectors that jointly detect a speaker’s spatial and temporal parameters. The corpus is suitable for various machine learning and signal processing tasks, linguistic studies, and studies related to a speaker’s fundamental frequency (due to recorded glottograms). Available corpora are limited to (synthetically generated/spatialized) speech data or recordings of musical instruments that lack moving speakers, glottograms, and/or multi-channel distant speech recordings. That is why we recorded 24 spatially non-moving and moving speakers, balanced male and female, to set up a two-room and 43-channel Austrian German multi-sensor speech corpus. It contains 8.2 hours of read speech based on phonetically balanced sentences, commands, and digits. The orthographic transcriptions include around 53,000 word tokens and 2,070 word types. Special features of this corpus are the laryngograph recordings (representing glottograms required to detect a speaker’s instantaneous fundamental frequency and pitch), corresponding clean-speech recordings, and spatial information and video data provided by four Kinects and a camera.

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A Database of Laryngeal High-Speed Videos with Simultaneous High-Quality Audio Recordings of Pathological and Non-Pathological Voices
Philipp Aichinger | Immer Roesner | Matthias Leonhard | Doris-Maria Denk-Linnert | Wolfgang Bigenzahn | Berit Schneider-Stickler

Auditory voice quality judgements are used intensively for the clinical assessment of pathological voice. Voice quality concepts are fuzzily defined and poorly standardized however, which hinders scientific and clinical communication. The described database documents a wide variety of pathologies and is used to investigate auditory voice quality concepts with regard to phonation mechanisms. The database contains 375 laryngeal high-speed videos and simultaneous high-quality audio recordings of sustained phonations of 80 pathological and 40 non-pathological subjects. Interval wise annotations regarding video and audio quality, as well as voice quality ratings are provided. Video quality is annotated for the visibility of anatomical structures and artefacts such as blurring or reduced contrast. Voice quality annotations include ratings on the presence of dysphonia and diplophonia. The purpose of the database is to aid the formulation of observationally well-founded models of phonation and the development of model-based automatic detectors for distinct types of phonation, especially for clinically relevant nonmodal voice phenomena. Another application is the training of audio-based fundamental frequency extractors on video-based reference fundamental frequencies.

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BulPhonC: Bulgarian Speech Corpus for the Development of ASR Technology
Neli Hateva | Petar Mitankin | Stoyan Mihov

In this paper we introduce a Bulgarian speech database, which was created for the purpose of ASR technology development. The paper describes the design and the content of the speech database. We present also an empirical evaluation of the performance of a LVCSR system for Bulgarian trained on the BulPhonC data. The resource is available free for scientific usage.

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Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian
Mārcis Pinnis | Askars Salimbajevs | Ilze Auziņa

In this paper the authors present a speech corpus designed and created for the development and evaluation of dictation systems in Latvian. The corpus consists of over nine hours of orthographically annotated speech from 30 different speakers. The corpus features spoken commands that are common for dictation systems for text editors. The corpus is evaluated in an automatic speech recognition scenario. Evaluation results in an ASR dictation scenario show that the addition of the corpus to the acoustic model training data in combination with language model adaptation allows to decrease the WER by up to relative 41.36% (or 16.83% in absolute numbers) compared to a baseline system without language model adaptation. Contribution of acoustic data augmentation is at relative 12.57% (or 3.43% absolute).

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The LetsRead Corpus of Portuguese Children Reading Aloud for Performance Evaluation
Jorge Proença | Dirce Celorico | Sara Candeias | Carla Lopes | Fernando Perdigão

This paper introduces the LetsRead Corpus of European Portuguese read speech from 6 to 10 years old children. The motivation for the creation of this corpus stems from the inexistence of databases with recordings of reading tasks of Portuguese children with different performance levels and including all the common reading aloud disfluencies. It is also essential to develop techniques to fulfill the main objective of the LetsRead project: to automatically evaluate the reading performance of children through the analysis of reading tasks. The collected data amounts to 20 hours of speech from 284 children from private and public Portuguese schools, with each child carrying out two tasks: reading sentences and reading a list of pseudowords, both with varying levels of difficulty throughout the school grades. In this paper, the design of the reading tasks presented to children is described, as well as the collection procedure. Manually annotated data is analyzed according to disfluencies and reading performance. The considered word difficulty parameter is also confirmed to be suitable for the pseudoword reading tasks.

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The BAS Speech Data Repository
Uwe Reichel | Florian Schiel | Thomas Kisler | Christoph Draxler | Nina Pörner

The BAS CLARIN speech data repository is introduced. At the current state it comprises 31 pre-dominantly German corpora of spoken language. It is compliant to the CLARIN-D as well as the OLAC requirements. This enables its embedding into several infrastructures. We give an overview over its structure, its implementation as well as the corpora it contains.

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A Dutch Dysarthric Speech Database for Individualized Speech Therapy Research
Emre Yilmaz | Mario Ganzeboom | Lilian Beijer | Catia Cucchiarini | Helmer Strik

We present a new Dutch dysarthric speech database containing utterances of neurological patients with Parkinson’s disease, traumatic brain injury and cerebrovascular accident. The speech content is phonetically and linguistically diversified by using numerous structured sentence and word lists. Containing more than 6 hours of mildly to moderately dysarthric speech, this database can be used for research on dysarthria and for developing and testing speech-to-text systems designed for medical applications. Current activities aimed at extending this database are also discussed.

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Urdu Summary Corpus
Muhammad Humayoun | Rao Muhammad Adeel Nawab | Muhammad Uzair | Saba Aslam | Omer Farzand

Language resources, such as corpora, are important for various natural language processing tasks. Urdu has millions of speakers around the world but it is under-resourced in terms of standard evaluation resources. This paper reports the construction of a benchmark corpus for Urdu summaries (abstracts) to facilitate the development and evaluation of single document summarization systems for Urdu language. In Urdu, space does not always mark word boundary. Therefore, we created two versions of the same corpus. In the first version, words are separated by space. In contrast, proper word boundaries are manually tagged in the second version. We further apply normalization, part-of-speech tagging, morphological analysis, lemmatization, and stemming for the articles and their summaries in both versions. In order to apply these annotations, we re-implemented some NLP tools for Urdu. We provide Urdu Summary Corpus, all these annotations and the needed software tools (as open-source) for researchers to run experiments and to evaluate their work including but not limited to single-document summarization task.

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A Publicly Available Indonesian Corpora for Automatic Abstractive and Extractive Chat Summarization
Fajri Koto

In this paper we report our effort to construct the first ever Indonesian corpora for chat summarization. Specifically, we utilized documents of multi-participant chat from a well known online instant messaging application, WhatsApp. We construct the gold standard by asking three native speakers to manually summarize 300 chat sections (152 of them contain images). As result, three reference summaries in extractive and either abstractive form are produced for each chat sections. The corpus is still in its early stage of investigation, yielding exciting possibilities of future works.

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Revisiting Summarization Evaluation for Scientific Articles
Arman Cohan | Nazli Goharian

Evaluation of text summarization approaches have been mostly based on metrics that measure similarities of system generated summaries with a set of human written gold-standard summaries. The most widely used metric in summarization evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps between the terms and phrases in the sentences; therefore, in cases of terminology variations and paraphrasing, ROUGE is not as effective. Scientific article summarization is one such case that is different from general domain summarization (e.g. newswire data). We provide an extensive analysis of ROUGE’s effectiveness as an evaluation metric for scientific summarization; we show that, contrary to the common belief, ROUGE is not much reliable in evaluating scientific summaries. We furthermore show how different variants of ROUGE result in very different correlations with the manual Pyramid scores. Finally, we propose an alternative metric for summarization evaluation which is based on the content relevance between a system generated summary and the corresponding human written summaries. We call our metric SERA (Summarization Evaluation by Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations with manual scores which shows its effectiveness in evaluation of scientific article summarization.

In this paper we present the OnForumS corpus developed for the shared task of the same name on Online Forum Summarisation (OnForumS at MultiLing’15). The corpus consists of a set of news articles with associated readers’ comments from The Guardian (English) and La Repubblica (Italian). It comes with four levels of annotation: argument structure, comment-article linking, sentiment and coreference. The former three were produced through crowdsourcing, whereas the latter, by an experienced annotator using a mature annotation scheme. Given its annotation breadth, we believe the corpus will prove a useful resource in stimulating and furthering research in the areas of Argumentation Mining, Summarisation, Sentiment, Coreference and the interlinks therein.

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Automatic Enrichment of WordNet with Common-Sense Knowledge
Luigi Di Caro | Guido Boella

WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.

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VPS-GradeUp: Graded Decisions on Usage Patterns
Vít Baisa | Silvie Cinková | Ema Krejčová | Anna Vernerová

We present VPS-GradeUp ― a set of 11,400 graded human decisions on usage patterns of 29 English lexical verbs from the Pattern Dictionary of English Verbs by Patrick Hanks. The annotation contains, for each verb lemma, a batch of 50 concordances with the given lemma as KWIC, and for each of these concordances we provide a graded human decision on how well the individual PDEV patterns for this particular lemma illustrate the given concordance, indicated on a 7-point Likert scale for each PDEV pattern. With our annotation, we were pursuing a pilot investigation of the foundations of human clustering and disambiguation decisions with respect to usage patterns of verbs in context. The data set is publicly available at http://hdl.handle.net/11234/1-1585.

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Sense-annotating a Lexical Substitution Data Set with Ubyline
Tristan Miller | Mohamed Khemakhem | Richard Eckart de Castilho | Iryna Gurevych

We describe the construction of GLASS, a newly sense-annotated version of the German lexical substitution data set used at the GermEval 2015: LexSub shared task. Using the two annotation layers, we conduct the first known empirical study of the relationship between manually applied word senses and lexical substitutions. We find that synonymy and hypernymy/hyponymy are the only semantic relations directly linking targets to their substitutes, and that substitutes in the target’s hypernymy/hyponymy taxonomy closely align with the synonyms of a single GermaNet synset. Despite this, these substitutes account for a minority of those provided by the annotators. The results of our analysis accord with those of a previous study on English-language data (albeit with automatically induced word senses), leading us to suspect that the sense―substitution relations we discovered may be of a universal nature. We also tentatively conclude that relatively cheap lexical substitution annotations can be used as a knowledge source for automatic WSD. Also introduced in this paper is Ubyline, the web application used to produce the sense annotations. Ubyline presents an intuitive user interface optimized for annotating lexical sample data, and is readily adaptable to sense inventories other than GermaNet.

We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts. Infinitive-verb compounds form a challenge for writers of German, because spelling regulations are different for literal and idiomatic uses. Through the participation of expert lexicographers we were able to obtain a high-quality corpus resource which offers itself as a testbed for automatic idiomaticity detection and coarse-grained word-sense disambiguation. We trained a classifier on the corpus which was able to distinguish literal and idiomatic uses with an accuracy of 85 %.

We launch the SemDaX corpus which is a recently completed Danish human-annotated corpus available through a CLARIN academic license. The corpus includes approx. 90,000 words, comprises six textual domains, and is annotated with sense inventories of different granularity. The aim of the developed corpus is twofold: i) to assess the reliability of the different sense annotation schemes for Danish measured by qualitative analyses and annotation agreement scores, and ii) to serve as training and test data for machine learning algorithms with the practical purpose of developing sense taggers for Danish. To these aims, we take a new approach to human-annotated corpus resources by double annotating a much larger part of the corpus than what is normally seen: for the all-words task we double annotated 60% of the material and for the lexical sample task 100%. We include in the corpus not only the adjucated files, but also the diverging annotations. In other words, we consider not all disagreement to be noise, but rather to contain valuable linguistic information that can help us improve our annotation schemes and our learning algorithms.

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Graded and Word-Sense-Disambiguation Decisions in Corpus Pattern Analysis: a Pilot Study
Silvie Cinková | Ema Krejčová | Anna Vernerová | Vít Baisa

We present a pilot analysis of a new linguistic resource, VPS-GradeUp (available at http://hdl.handle.net/11234/1-1585). The resource contains 11,400 graded human decisions on usage patterns of 29 English lexical verbs, randomly selected from the Pattern Dictionary of English Verbs (Hanks, 2000 2014) based on their frequency and the number of senses their lemmas have in PDEV. This data set has been created to observe the interannotator agreement on PDEV patterns produced using the Corpus Pattern Analysis (Hanks, 2013). Apart from the graded decisions, the data set also contains traditional Word-Sense-Disambiguation (WSD) labels. We analyze the associations between the graded annotation and WSD annotation. The results of the respective annotations do not correlate with the size of the usage pattern inventory for the respective verbs lemmas, which makes the data set worth further linguistic analysis.

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Multi-prototype Chinese Character Embedding
Yanan Lu | Yue Zhang | Donghong Ji

Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74% F-score improvement over a state-of-the-art baseline.

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A comparison of Named-Entity Disambiguation and Word Sense Disambiguation
Angel Chang | Valentin I. Spitkovsky | Christopher D. Manning | Eneko Agirre

Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense disambiguation (WSD), where the mention of an open-class word is linked to a concept in a knowledge-base, typically WordNet. This paper analyzes the relation between two annotated datasets on NED and WSD, highlighting the commonalities and differences. We detail the methods to construct a NED system following the WSD word-expert approach, where we need a dictionary and one classifier is built for each target entity mention string. Constructing a dictionary for NED proved challenging, and although similarity and ambiguity are higher for NED, the results are also higher due to the larger number of training data, and the more crisp and skewed meaning differences.

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Leveraging RDF Graphs for Crossing Multiple Bilingual Dictionaries
Marta Villegas | Maite Melero | Núria Bel | Jorge Gracia

The experiments presented here exploit the properties of the Apertium RDF Graph, principally cycle density and nodes’ degree, to automatically generate new translation relations between words, and therefore to enrich existing bilingual dictionaries with new entries. Currently, the Apertium RDF Graph includes data from 22 Apertium bilingual dictionaries and constitutes a large unified array of linked lexical entries and translations that are available and accessible on the Web (http://linguistic.linkeddata.es/apertium/). In particular, its graph structure allows for interesting exploitation opportunities, some of which are addressed in this paper. Two ‘massive’ experiments are reported: in the first one, the original EN-ES translation set was removed from the Apertium RDF Graph and a new EN-ES version was generated. The results were compared against the previously removed EN-ES data and against the Concise Oxford Spanish Dictionary. In the second experiment, a new non-existent EN-FR translation set was generated. In this case the results were compared against a converted wiktionary English-French file. The results we got are really good and perform well for the extreme case of correlated polysemy. This lead us to address the possibility to use cycles and nodes degree to identify potential oddities in the source data. If cycle density proves efficient when considering potential targets, we can assume that in dense graphs nodes with low degree may indicate potential errors.

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PreMOn: a Lemon Extension for Exposing Predicate Models as Linked Data
Francesco Corcoglioniti | Marco Rospocher | Alessio Palmero Aprosio | Sara Tonelli

We introduce PreMOn (predicate model for ontologies), a linguistic resource for exposing predicate models (PropBank, NomBank, VerbNet, and FrameNet) and mappings between them (e.g, SemLink) as Linked Open Data. It consists of two components: (i) the PreMOn Ontology, an extension of the lemon model by the W3C Ontology-Lexica Community Group, that enables to homogeneously represent data from the various predicate models; and, (ii) the PreMOn Dataset, a collection of RDF datasets integrating various versions of the aforementioned predicate models and mapping resources. PreMOn is freely available and accessible online in different ways, including through a dedicated SPARQL endpoint.

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Semantic Links for Portuguese
Fabricio Chalub | Livy Real | Alexandre Rademaker | Valeria de Paiva

This paper describes work on incorporating Princenton’s WordNet morphosemantics links to the fabric of the Portuguese OpenWordNet-PT. Morphosemantic links are relations between verbs and derivationally related nouns that are semantically typed (such as for tune-tuner ― in Portuguese “afinar-afinador” – linked through an “agent” link). Morphosemantic links have been discussed for Princeton’s WordNet for a while, but have not been added to the official database. These links are very useful, they help us to improve our Portuguese WordNet. Thus we discuss the integration of these links in our base and the issues we encountered with the integration.

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Creating Linked Data Morphological Language Resources with MMoOn - The Hebrew Morpheme Inventory
Bettina Klimek | Natanael Arndt | Sebastian Krause | Timotheus Arndt

The development of standard models for describing general lexical resources has led to the emergence of numerous lexical datasets of various languages in the Semantic Web. However, equivalent models covering the linguistic domain of morphology do not exist. As a result, there are hardly any language resources of morphemic data available in RDF to date. This paper presents the creation of the Hebrew Morpheme Inventory from a manually compiled tabular dataset comprising around 52.000 entries. It is an ongoing effort of representing the lexemes, word-forms and morphologigal patterns together with their underlying relations based on the newly created Multilingual Morpheme Ontology (MMoOn). It will be shown how segmented Hebrew language data can be granularly described in a Linked Data format, thus, serving as an exemplary case for creating morpheme inventories of any inflectional language with MMoOn. The resulting dataset is described a) according to the structure of the underlying data format, b) with respect to the Hebrew language characteristic of building word-forms directly from roots, c) by exemplifying how inflectional information is realized and d) with regard to its enrichment with external links to sense resources.

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Parallel Global Voices: a Collection of Multilingual Corpora with Citizen Media Stories
Prokopis Prokopidis | Vassilis Papavassiliou | Stelios Piperidis

We present a new collection of multilingual corpora automatically created from the content available in the Global Voices websites, where volunteers have been posting and translating citizen media stories since 2004. We describe how we crawled and processed this content to generate parallel resources comprising 302.6K document pairs and 8.36M segment alignments in 756 language pairs. For some language pairs, the segment alignments in this resource are the first open examples of their kind. In an initial use of this resource, we discuss how a set of document pair detection algorithms performs on the Greek-English corpus.

High accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language processing (NLP) applications to new social media as well as diverse language adaptation. Effort launched by the BOLT (Broad Operational Language Translation) program at DARPA (Defense Advanced Research Projects Agency) successfully addressed the internet information with enhanced NLP systems. BOLT aims for automated translation and linguistic analysis for informal genres of text and speech in online and in-person communication. As a part of this program, the Linguistic Data Consortium (LDC) developed valuable linguistic resources in support of the training and evaluation of such new technologies. This paper focuses on methodologies, infrastructure, and procedure for developing linguistic annotation at various language levels, including Treebank (TB), word alignment (WA), PropBank (PB), and co-reference (CoRef). Inspired by the OntoNotes approach with adaptations to the tasks to reflect the goals and scope of the BOLT project, this effort has introduced more annotation types of informal and free-style genres in English, Chinese and Egyptian Arabic. The corpus produced is by far the largest multi-lingual, multi-level and multi-genre annotation corpus of informal text and speech.

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C4Corpus: Multilingual Web-size Corpus with Free License
Ivan Habernal | Omnia Zayed | Iryna Gurevych

Large Web corpora containing full documents with permissive licenses are crucial for many NLP tasks. In this article we present the construction of 12 million-pages Web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs. Our highly-scalable Hadoop-based framework is able to process the full CommonCrawl corpus on 2000+ CPU cluster on the Amazon Elastic Map/Reduce infrastructure. The processing pipeline includes license identification, state-of-the-art boilerplate removal, exact duplicate and near-duplicate document removal, and language detection. The construction of the corpus is highly configurable and fully reproducible, and we provide both the framework (DKPro C4CorpusTools) and the resulting data (C4Corpus) to the research community.

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OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles
Pierre Lison | Jörg Tiedemann

We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

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LexFr: Adapting the LexIt Framework to Build a Corpus-based French Subcategorization Lexicon
Giulia Rambelli | Gianluca Lebani | Laurent Prévot | Alessandro Lenci

This paper introduces LexFr, a corpus-based French lexical resource built by adapting the framework LexIt, originally developed to describe the combinatorial potential of Italian predicates. As in the original framework, the behavior of a group of target predicates is characterized by a series of syntactic (i.e., subcategorization frames) and semantic (i.e., selectional preferences) statistical information (a.k.a. distributional profiles) whose extraction process is mostly unsupervised. The first release of LexFr includes information for 2,493 verbs, 7,939 nouns and 2,628 adjectives. In these pages we describe the adaptation process and evaluated the final resource by comparing the information collected for 20 test verbs against the information available in a gold standard dictionary. In the best performing setting, we obtained 0.74 precision, 0.66 recall and 0.70 F-measure.

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Polarity Lexicon Building: to what Extent Is the Manual Effort Worth?
Iñaki San Vicente | Xabier Saralegi

Polarity lexicons are a basic resource for analyzing the sentiments and opinions expressed in texts in an automated way. This paper explores three methods to construct polarity lexicons: translating existing lexicons from other languages, extracting polarity lexicons from corpora, and annotating sentiments Lexical Knowledge Bases. Each of these methods require a different degree of human effort. We evaluate how much manual effort is needed and to what extent that effort pays in terms of performance improvement. Experiment setup includes generating lexicons for Basque, and evaluating them against gold standard datasets in different domains. Results show that extracting polarity lexicons from corpora is the best solution for achieving a good performance with reasonable human effort.

This paper describes the conversion into LMF, a standard lexicographic digital format of ‘al-qāmūs al-muḥīṭ, a Medieval Arabic lexicon. The lexicon is first described, then all the steps required for the conversion are illustrated. The work is will produce a useful lexicographic resource for Arabic NLP, but is also interesting per se, to study the implications of adapting the LMF model to the Arabic language. Some reflections are offered as to the status of roots with respect to previously suggested representations. In particular, roots are, in our opinion are to be not treated as lexical entries, but modeled as lexical metadata for classifying and identifying lexical entries. In this manner, each root connects all entries that are derived from it.

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CASSAurus: A Resource of Simpler Spanish Synonyms
Ricardo Baeza-Yates | Luz Rello | Julia Dembowski

In this work we introduce and describe a language resource composed of lists of simpler synonyms for Spanish. The synonyms are divided in different senses taken from the Spanish OpenThesaurus, where context disambiguation was performed by using statistical information from the Web and Google Books Ngrams. This resource is freely available online and can be used for different NLP tasks such as lexical simplification. Indeed, so far it has been already integrated into four tools.

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Measuring Lexical Quality of a Historical Finnish Newspaper Collection ― Analysis of Garbled OCR Data with Basic Language Technology Tools and Means
Kimmo Kettunen | Tuula Pääkkönen

The National Library of Finland has digitized a large proportion of the historical newspapers published in Finland between 1771 and 1910 (Bremer-Laamanen 2001). This collection contains approximately 1.95 million pages in Finnish and Swedish. Finnish part of the collection consists of about 2.39 billion words. The National Library’s Digital Collections are offered via the digi.kansalliskirjasto.fi web service, also known as Digi. Part of this material is also available freely downloadable in The Language Bank of Finland provided by the Fin-CLARIN consortium . The collection can also be accessed through the Korp environment that has been developed by Spräkbanken at the University of Gothenburg and extended by FIN-CLARIN team at the University of Helsinki to provide concordances of text resources. A Cranfield-style information retrieval test collection has been produced out of a small part of the Digi newspaper material at the University of Tampere (Järvelin et al., 2015). The quality of the OCRed collections is an important topic in digital humanities, as it affects general usability and searchability of collections. There is no single available method to assess the quality of large collections, but different methods can be used to approximate the quality. This paper discusses different corpus analysis style ways to approximate the overall lexical quality of the Finnish part of the Digi collection.

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Using SMT for OCR Error Correction of Historical Texts
Haithem Afli | Zhengwei Qiu | Andy Way | Páraic Sheridan

A trend to digitize historical paper-based archives has emerged in recent years, with the advent of digital optical scanners. A lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into electronic versions that can be manipulated by a computer. For this purpose, Optical Character Recognition (OCR) systems have been developed to transform scanned digital text into editable computer text. However, different kinds of errors in the OCR system output text can be found, but Automatic Error Correction tools can help in performing the quality of electronic texts by cleaning and removing noises. In this paper, we perform a qualitative and quantitative comparison of several error-correction techniques for historical French documents. Experimentation shows that our Machine Translation for Error Correction method is superior to other Language Modelling correction techniques, with nearly 13% relative improvement compared to the initial baseline.

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OCR Post-Correction Evaluation of Early Dutch Books Online - Revisited
Martin Reynaert

We present further work on evaluation of the fully automatic post-correction of Early Dutch Books Online, a collection of 10,333 18th century books. In prior work we evaluated the new implementation of Text-Induced Corpus Clean-up (TICCL) on the basis of a single book Gold Standard derived from this collection. In the current paper we revisit the same collection on the basis of a sizeable 1020 item random sample of OCR post-corrected strings from the full collection. Both evaluations have their own stories to tell and lessons to teach.

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Crowdsourcing an OCR Gold Standard for a German and French Heritage Corpus
Simon Clematide | Lenz Furrer | Martin Volk

Crowdsourcing approaches for post-correction of OCR output (Optical Character Recognition) have been successfully applied to several historic text collections. We report on our crowd-correction platform Kokos, which we built to improve the OCR quality of the digitized yearbooks of the Swiss Alpine Club (SAC) from the 19th century. This multilingual heritage corpus consists of Alpine texts mainly written in German and French, all typeset in Antiqua font. Finding and engaging volunteers for correcting large amounts of pages into high quality text requires a carefully designed user interface, an easy-to-use workflow, and continuous efforts for keeping the participants motivated. More than 180,000 characters on about 21,000 pages were corrected by volunteers in about 7 month, achieving an OCR gold standard with a systematically evaluated accuracy of 99.7% on the word level. The crowdsourced OCR gold standard and the corresponding original OCR recognition results from Abby FineReader 7 for each page are available as a resource. Additionally, the scanned images (300dpi) of all pages are included in order to facilitate tests with other OCR software.

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Argument Mining: the Bottleneck of Knowledge and Language Resources
Patrick Saint-Dizier

Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus knowledge driven.

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From Interoperable Annotations towards Interoperable Resources: A Multilingual Approach to the Analysis of Discourse
Ekaterina Lapshinova-Koltunski | Kerstin Anna Kunz | Anna Nedoluzhko

In the present paper, we analyse variation of discourse phenomena in two typologically different languages, i.e. in German and Czech. The novelty of our approach lies in the nature of the resources we are using. Advantage is taken of existing resources, which are, however, annotated on the basis of two different frameworks. We use an interoperable scheme unifying discourse phenomena in both frameworks into more abstract categories and considering only those phenomena that have a direct match in German and Czech. The discourse properties we focus on are relations of identity, semantic similarity, ellipsis and discourse relations. Our study shows that the application of interoperable schemes allows an exploitation of discourse-related phenomena analysed in different projects and on the basis of different frameworks. As corpus compilation and annotation is a time-consuming task, positive results of this experiment open up new paths for contrastive linguistics, translation studies and NLP, including machine translation.

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Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans
Henk van den Heuvel | Nelleke Oostdijk

In sources used in oral history research (such as interviews with eye witnesses), passages where the degree of personal emotional involvement is found to be high can be of particular interest, as these may give insight into how historical events were experienced, and what moral dilemmas and psychological or religious struggles were encountered. In a pilot study involving a large corpus of interview recordings with Dutch war veterans, we have investigated if it is possible to develop a method for automatically identifying those passages where the degree of personal emotional involvement is high. The method is based on the automatic detection of exceptionally large silences and filled pause segments (using Automatic Speech Recognition), and cues taken from specific n-grams. The first results appear to be encouraging enough for further elaboration of the method.

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A Bilingual Discourse Corpus and Its Applications
Yang Liu | Jiajun Zhang | Chengqing Zong | Yating Yang | Xi Zhou

Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation. To address this issue, we design and build a bilingual discource corpus in which we are currently defining and annotating the bilingual elementary discourse units (BEDUs). The BEDUs are then organized into hierarchical structures. Using this discourse style, we have annotated nearly 20K LDC sentences. Finally, we design a bilingual discourse based method for machine translation evaluation and show the effectiveness of our bilingual discourse annotations.

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Adding Semantic Relations to a Large-Coverage Connective Lexicon of German
Tatjana Scheffler | Manfred Stede

DiMLex is a lexicon of German connectives that can be used for various language understanding purposes. We enhanced the coverage to 275 connectives, which we regard as covering all known German discourse connectives in current use. In this paper, we consider the task of adding the semantic relations that can be expressed by each connective. After discussing different approaches to retrieving semantic information, we settle on annotating each connective with senses from the new PDTB 3.0 sense hierarchy. We describe our new implementation in the extended DiMLex, which will be available for research purposes.

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Corpus Resources for Dispute Mediation Discourse
Mathilde Janier | Chris Reed

Dispute mediation is a growing activity in the resolution of conflicts, and more and more research emerge to enhance and better understand this (until recently) understudied practice. Corpus analyses are necessary to study discourse in this context; yet, little data is available, mainly because of its confidentiality principle. After proposing hints and avenues to acquire transcripts of mediation sessions, this paper presents the Dispute Mediation Corpus, which gathers annotated excerpts of mediation dialogues. Although developed as part of a project on argumentation, it is freely available and the text data can be used by anyone. This first-ever open corpus of mediation interactions can be of interest to scholars studying discourse, but also conflict resolution, argumentation, linguistics, communication, etc. We advocate for using and extending this resource that may be valuable to a large variety of domains of research, particularly those striving to enhance the study of the rapidly growing activity of dispute mediation.

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A Tagged Corpus for Automatic Labeling of Disabilities in Medical Scientific Papers
Carlos Valmaseda | Juan Martinez-Romo | Lourdes Araujo

This paper presents the creation of a corpus of labeled disabilities in scientific papers. The identification of medical concepts in documents and, especially, the identification of disabilities, is a complex task mainly due to the variety of expressions that can make reference to the same problem. Currently there is not a set of documents manually annotated with disabilities with which to evaluate an automatic detection system of such concepts. This is the reason why this corpus arises, aiming to facilitate the evaluation of systems that implement an automatic annotation tool for extracting biomedical concepts such as disabilities. The result is a set of scientific papers manually annotated. For the selection of these scientific papers has been conducted a search using a list of rare diseases, since they generally have associated several disabilities of different kinds.

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PersonaBank: A Corpus of Personal Narratives and Their Story Intention Graphs
Stephanie Lukin | Kevin Bowden | Casey Barackman | Marilyn Walker

We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their Story Intention Graphs, a deep representation of the content of a story. We describe the topics of the stories and the basis of the Story Intention Graph representation, as well as the process of annotating the stories to produce the Story Intention Graphs and the challenges of adapting the tool to this new personal narrative domain. We also discuss how the corpus can be used in applications that retell the story using different styles of tellings, co-tellings, or as a content planner.

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Fine-Grained Chinese Discourse Relation Labelling
Huan-Yuan Chen | Wan-Shan Liao | Hen-Hsen Huang | Hsin-Hsi Chen

This paper explores several aspects together for a fine-grained Chinese discourse analysis. We deal with the issues of ambiguous discourse markers, ambiguous marker linkings, and more than one discourse marker. A universal feature representation is proposed. The pair-once postulation, cross-discourse-unit-first rule and word-pair-marker-first rule select a set of discourse markers from ambiguous linkings. Marker-Sum feature considers total contribution of markers and Marker-Preference feature captures the probability distribution of discourse functions of a representative marker by using preference rule. The HIT Chinese discourse relation treebank (HIT-CDTB) is used to evaluate the proposed models. The 25-way classifier achieves 0.57 micro-averaged F-score.

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Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks
Ines Rehbein | Merel Scholman | Vera Demberg

In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.

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Enhancing The RATP-DECODA Corpus With Linguistic Annotations For Performing A Large Range Of NLP Tasks
Carole Lailler | Anaïs Landeau | Frédéric Béchet | Yannick Estève | Paul Deléglise

In this article, we present the RATP-DECODA Corpus which is composed by a set of 67 hours of speech from telephone conversations of a Customer Care Service (CCS). This corpus is already available on line at http://sldr.org/sldr000847/fr in its first version. However, many enhancements have been made in order to allow the development of automatic techniques to transcript conversations and to capture their meaning. These enhancements fall into two categories: firstly, we have increased the size of the corpus with manual transcriptions from a new operational day; secondly we have added new linguistic annotations to the whole corpus (either manually or through an automatic processing) in order to perform various linguistic tasks from syntactic and semantic parsing to dialog act tagging and dialog summarization.

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Parallel Discourse Annotations on a Corpus of Short Texts
Manfred Stede | Stergos Afantenos | Andreas Peldszus | Nicholas Asher | Jérémy Perret

We present the first corpus of texts annotated with two alternative approaches to discourse structure, Rhetorical Structure Theory (Mann and Thompson, 1988) and Segmented Discourse Representation Theory (Asher and Lascarides, 2003). 112 short argumentative texts have been analyzed according to these two theories. Furthermore, in previous work, the same texts have already been annotated for their argumentation structure, according to the scheme of Peldszus and Stede (2013). This corpus therefore enables studies of correlations between the two accounts of discourse structure, and between discourse and argumentation. We converted the three annotation formats to a common dependency tree format that enables to compare the structures, and we describe some initial findings.

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An Annotated Corpus of Direct Speech
John Lee | Chak Yan Yeung

We propose a scheme for annotating direct speech in literary texts, based on the Text Encoding Initiative (TEI) and the coreference annotation guidelines from the Message Understanding Conference (MUC). The scheme encodes the speakers and listeners of utterances in a text, as well as the quotative verbs that reports the utterances. We measure inter-annotator agreement on this annotation task. We then present statistics on a manually annotated corpus that consists of books from the New Testament. Finally, we visualize the corpus as a conversational network.

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Evaluating the Noisy Channel Model for the Normalization of Historical Texts: Basque, Spanish and Slovene
Izaskun Etxeberria | Iñaki Alegria | Larraitz Uria | Mans Hulden

This paper presents a method for the normalization of historical texts using a combination of weighted finite-state transducers and language models. We have extended our previous work on the normalization of dialectal texts and tested the method against a 17th century literary work in Basque. This preprocessed corpus is made available in the LREC repository. The performance of this method for learning relations between historical and contemporary word forms is evaluated against resources in three languages. The method we present learns to map phonological changes using a noisy channel model. The model is based on techniques commonly used for phonological inference and producing Grapheme-to-Grapheme conversion systems encoded as weighted transducers and produces F-scores above 80% in the task for Basque. A wider evaluation shows that the approach performs equally well with all the languages in our evaluation suite: Basque, Spanish and Slovene. A comparison against other methods that address the same task is also provided.

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Farasa: A New Fast and Accurate Arabic Word Segmenter
Kareem Darwish | Hamdy Mubarak

In this paper, we present Farasa (meaning insight in Arabic), which is a fast and accurate Arabic segmenter. Segmentation involves breaking Arabic words into their constituent clitics. Our approach is based on SVMrank using linear kernels. The features that we utilized account for: likelihood of stems, prefixes, suffixes, and their combination; presence in lexicons containing valid stems and named entities; and underlying stem templates. Farasa outperforms or equalizes state-of-the-art Arabic segmenters, namely QATARA and MADAMIRA. Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA. The segmenter should be able to process one billion words in less than 5 hours. Farasa is written entirely in native Java, with no external dependencies, and is open-source.

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A Morphological Lexicon of Esperanto with Morpheme Frequencies
Eckhard Bick

This paper discusses the internal structure of complex Esperanto words (CWs). Using a morphological analyzer, possible affixation and compounding is checked for over 50,000 Esperanto lexemes against a list of 17,000 root words. Morpheme boundaries in the resulting analyses were then checked manually, creating a CW dictionary of 28,000 words, representing 56.4% of the lexicon, or 19.4% of corpus tokens. The error percentage of the EspGram morphological analyzer for new corpus CWs was 4.3% for types and 6.4% for tokens, with a recall of almost 100%, and wrong/spurious boundaries being more common than missing ones. For pedagogical purposes a morpheme frequency dictionary was constructed for a 16 million word corpus, confirming the importance of agglutinative derivational morphemes in the Esperanto lexicon. Finally, as a means to reduce the morphological ambiguity of CWs, we provide POS likelihoods for Esperanto suffixes.

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How does Dictionary Size Influence Performance of Vietnamese Word Segmentation?
Wuying Liu | Lin Wang

Vietnamese word segmentation (VWS) is a challenging basic issue for natural language processing. This paper addresses the problem of how does dictionary size influence VWS performance, proposes two novel measures: square overlap ratio (SOR) and relaxed square overlap ratio (RSOR), and validates their effectiveness. The SOR measure is the product of dictionary overlap ratio and corpus overlap ratio, and the RSOR measure is the relaxed version of SOR measure under an unsupervised condition. The two measures both indicate the suitable degree between segmentation dictionary and object corpus waiting for segmentation. The experimental results show that the more suitable, neither smaller nor larger, dictionary size is better to achieve the state-of-the-art performance for dictionary-based Vietnamese word segmenters.

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Giving Lexical Resources a Second Life: Démonette, a Multi-sourced Morpho-semantic Network for French
Nabil Hathout | Fiammetta Namer

Démonette is a derivational morphological network designed for the description of French. Its original architecture enables its use as a formal framework for the description of morphological analyses and as a repository for existing lexicons. It is fed with a variety of resources, which all are already validated. The harmonization of their content into a unified format provides them a second life, in which they are enriched with new properties, provided these are deductible from their contents. Démonette is released under a Creative Commons license. It is usable for theoretical and descriptive research in morphology, as a source of experimental material for psycholinguistics, natural language processing (NLP) and information retrieval (IR), where it fills a gap, since French lacks a large-coverage derivational resources database. The article presents the integration of two existing lexicons into Démonette. The first is Verbaction, a lexicon of deverbal action nouns. The second is Lexeur, a database of agent nouns in -eur derived from verbs or from nouns.

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Syntactic Analysis of Phrasal Compounds in Corpora: a Challenge for NLP Tools
Carola Trips

The paper introduces a “train once, use many” approach for the syntactic analysis of phrasal compounds (PC) of the type XP+N like “Would you like to sit on my knee?” nonsense. PCs are a challenge for NLP tools since they require the identification of a syntactic phrase within a morphological complex. We propose a method which uses a state-of-the-art dependency parser not only to analyse sentences (the environment of PCs) but also to compound the non-head of PCs in a well-defined particular condition which is the analysis of the non-head spanning from the left boundary (mostly marked by a determiner) to the nominal head of the PC. This method contains the following steps: (a) the use an English state-of-the-art dependency parser with data comprising sentences with PCs from the British National Corpus (BNC), (b) the detection of parsing errors of PCs, (c) the separate treatment of the non-head structure using the same model, and (d) the attachment of the non-head to the compound head. The evaluation of the method showed that the accuracy of 76% could be improved by adding a step in the PC compounder module which specified user-defined contexts being sensitive to the part of speech of the non-head parts and by using TreeTagger, in line with our approach.

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DALILA: The Dialectal Arabic Linguistic Learning Assistant
Salam Khalifa | Houda Bouamor | Nizar Habash

Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of tools to support learning Arabic as a living language on the web, where authentic material can be found in both MSA and DA. In this paper, we present the Dialectal Arabic Linguistic Learning Assistant (DALILA), a Chrome extension that utilizes cutting-edge Arabic dialect NLP research to assist learners and non-native speakers in understanding text written in either MSA or DA. DALILA provides dialectal word analysis and English gloss corresponding to each word.

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Refurbishing a Morphological Database for German
Petra Steiner

The CELEX database is one of the standard lexical resources for German. It yields a wealth of data especially for phonological and morphological applications. The morphological part comprises deep-structure morphological analyses of German. However, as it was developed in the Nineties, both encoding and spelling are outdated. About one fifth of over 50,000 datasets contain umlauts and signs such as ß. Changes to a modern version cannot be obtained by simple substitution. In this paper, we shortly describe the original content and form of the orthographic and morphological database for German in CELEX. Then we present our work on modernizing the linguistic data. Lemmas and morphological analyses are transferred to a modern standard of encoding by first merging orthographic and morphological information of the lemmas and their entries and then performing a second substitution for the morphs within their morphological analyses. Changes to modern German spelling are performed by substitution rules according to orthographical standards. We show an example of the use of the data for the disambiguation of morphological structures. The discussion describes prospects of future work on this or similar lexicons. The Perl script is publicly available on our website.

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Encoding Adjective Scales for Fine-grained Resources
Cédric Lopez | Frédérique Segond | Christiane Fellbaum

We propose an automatic approach towards determining the relative location of adjectives on a common scale based on their strength. We focus on adjectives expressing different degrees of goodness occurring in French product (perfumes) reviews. Using morphosyntactic patterns, we extract from the reviews short phrases consisting of a noun that encodes a particular aspect of the perfume and an adjective modifying that noun. We then associate each such n-gram with the corresponding product aspect and its related star rating. Next, based on the star scores, we generate adjective scales reflecting the relative strength of specific adjectives associated with a shared attribute of the product. An automatic ordering of the adjectives “correct” (correct), “sympa” (nice), “bon” (good) and “excellent” (excellent) according to their score in our resource is consistent with an intuitive scale based on human judgments. Our long-term objective is to generate different adjective scales in an empirical manner, which could allow the enrichment of lexical resources.

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SCARE ― The Sentiment Corpus of App Reviews with Fine-grained Annotations in German
Mario Sänger | Ulf Leser | Steffen Kemmerer | Peter Adolphs | Roman Klinger

The automatic analysis of texts containing opinions of users about, e.g., products or political views has gained attention within the last decades. However, previous work on the task of analyzing user reviews about mobile applications in app stores is limited. Publicly available corpora do not exist, such that a comparison of different methods and models is difficult. We fill this gap by contributing the Sentiment Corpus of App Reviews (SCARE), which contains fine-grained annotations of application aspects, subjective (evaluative) phrases and relations between both. This corpus consists of 1,760 annotated application reviews from the Google Play Store with 2,487 aspects and 3,959 subjective phrases. We describe the process and methodology how the corpus was created. The Fleiss Kappa between four annotators reveals an agreement of 0.72. We provide a strong baseline with a linear-chain conditional random field and word-embedding features with a performance of 0.62 for aspect detection and 0.63 for the extraction of subjective phrases. The corpus is available to the research community to support the development of sentiment analysis methods on mobile application reviews.

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Datasets for Aspect-Based Sentiment Analysis in French
Marianna Apidianaki | Xavier Tannier | Cécile Richart

Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 “Aspect-Based Sentiment Analysis” where seven different languages were represented, and are publicly available for research purposes.

In this article we describe our method of automatically expanding an existing lexicon of words with affective valence scores. The automatic expansion process was done in English. In addition, we describe our procedure for automatically creating lexicons in languages where such resources may not previously exist. The foreign languages we discuss in this paper are Spanish, Russian and Farsi. We also describe the procedures to systematically validate our newly created resources. The main contributions of this work are: 1) A general method for expansion and creation of lexicons with scores of words on psychological constructs such as valence, arousal or dominance; and 2) a procedure for ensuring validity of the newly constructed resources.

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PotTS: The Potsdam Twitter Sentiment Corpus
Uladzimir Sidarenka

In this paper, we introduce a novel comprehensive dataset of 7,992 German tweets, which were manually annotated by two human experts with fine-grained opinion relations. A rich annotation scheme used for this corpus includes such sentiment-relevant elements as opinion spans, their respective sources and targets, emotionally laden terms with their possible contextual negations and modifiers. Various inter-annotator agreement studies, which were carried out at different stages of work on these data (at the initial training phase, upon an adjudication step, and after the final annotation run), reveal that labeling evaluative judgements in microblogs is an inherently difficult task even for professional coders. These difficulties, however, can be alleviated by letting the annotators revise each other’s decisions. Once rechecked, the experts can proceed with the annotation of further messages, staying at a fairly high level of agreement.

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Challenges of Evaluating Sentiment Analysis Tools on Social Media
Diana Maynard | Kalina Bontcheva

This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation results, this paper focuses on tweet sentiment analysis in particular. One existing and two newly created corpora are used, and the performance of four different sentiment analysis systems is reported; we make our annotated datasets and sentiment analysis applications publicly available. We see considerable variations in results across the different corpora, which calls into question the validity of many existing annotated datasets and evaluations, and we make some observations about both the systems and the datasets as a result.

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EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis
Jasy Suet Yan Liew | Howard R. Turtle | Elizabeth D. Liddy

This paper describes EmoTweet-28, a carefully curated corpus of 15,553 tweets annotated with 28 emotion categories for the purpose of training and evaluating machine learning models for emotion classification. EmoTweet-28 is, to date, the largest tweet corpus annotated with fine-grained emotion categories. The corpus contains annotations for four facets of emotion: valence, arousal, emotion category and emotion cues. We first used small-scale content analysis to inductively identify a set of emotion categories that characterize the emotions expressed in microblog text. We then expanded the size of the corpus using crowdsourcing. The corpus encompasses a variety of examples including explicit and implicit expressions of emotions as well as tweets containing multiple emotions. EmoTweet-28 represents an important resource to advance the development and evaluation of more emotion-sensitive systems.

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Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases
Svetlana Kiritchenko | Saif Mohammad

Sentiment composition is the determining of sentiment of a multi-word linguistic unit, such as a phrase or a sentence, based on its constituents. We focus on sentiment composition in phrases formed by at least one positive and at least one negative word ― phrases like ‘happy accident’ and ‘best winter break’. We refer to such phrases as opposing polarity phrases. We manually annotate a collection of opposing polarity phrases and their constituent single words with real-valued sentiment intensity scores using a method known as Best―Worst Scaling. We show that the obtained annotations are consistent. We explore the entries in the lexicon for linguistic regularities that govern sentiment composition in opposing polarity phrases. Finally, we list the current and possible future applications of the lexicon.

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Detecting Implicit Expressions of Affect from Text using Semantic Knowledge on Common Concept Properties
Alexandra Balahur | Hristo Tanev

Emotions are an important part of the human experience. They are responsible for the adaptation and integration in the environment, offering, most of the time together with the cognitive system, the appropriate responses to stimuli in the environment. As such, they are an important component in decision-making processes. In today’s society, the avalanche of stimuli present in the environment (physical or virtual) makes people more prone to respond to stronger affective stimuli (i.e., those that are related to their basic needs and motivations ― survival, food, shelter, etc.). In media reporting, this is translated in the use of arguments (factual data) that are known to trigger specific (strong, affective) behavioural reactions from the readers. This paper describes initial efforts to detect such arguments from text, based on the properties of concepts. The final system able to retrieve and label this type of data from the news in traditional and social platforms is intended to be integrated Europe Media Monitor family of applications to detect texts that trigger certain (especially negative) reactions from the public, with consequences on citizen safety and security.

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Creating a General Russian Sentiment Lexicon
Natalia Loukachevitch | Anatolii Levchik

The paper describes the new Russian sentiment lexicon - RuSentiLex. The lexicon was gathered from several sources: opinionated words from domain-oriented Russian sentiment vocabularies, slang and curse words extracted from Twitter, objective words with positive or negative connotations from a news collection. The words in the lexicon having different sentiment orientations in specific senses are linked to appropriate concepts of the thesaurus of Russian language RuThes. All lexicon entries are classified according to four sentiment categories and three sources of sentiment (opinion, emotion, or fact). The lexicon can serve as the first version for the construction of domain-specific sentiment lexicons or can be used for feature generation in machine-learning approaches. In this role, the RuSentiLex lexicon was utilized by the participants of the SentiRuEval-2016 Twitter reputation monitoring shared task and allowed them to achieve high results.

This paper presents a framework and methodology for the annotation of perspectives in text. In the last decade, different aspects of linguistic encoding of perspectives have been targeted as separated phenomena through different annotation initiatives. We propose an annotation scheme that integrates these different phenomena. We use a multilayered annotation approach, splitting the annotation of different aspects of perspectives into small subsequent subtasks in order to reduce the complexity of the task and to better monitor interactions between layers. Currently, we have included four layers of perspective annotation: events, attribution, factuality and opinion. The annotations are integrated in a formal model called GRaSP, which provides the means to represent instances (e.g. events, entities) and propositions in the (real or assumed) world in relation to their mentions in text. Then, the relation between the source and target of a perspective is characterized by means of perspective annotations. This enables us to place alternative perspectives on the same entity, event or proposition next to each other.

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Integration of Lexical and Semantic Knowledge for Sentiment Analysis in SMS
Wejdene Khiari | Mathieu Roche | Asma Bouhafs Hafsia

With the explosive growth of online social media (forums, blogs, and social networks), exploitation of these new information sources has become essential. Our work is based on the sud4science project. The goal of this project is to perform multidisciplinary work on a corpus of authentic SMS, in French, collected in 2011 and anonymised (88milSMS corpus: http://88milsms.huma-num.fr). This paper highlights a new method to integrate opinion detection knowledge from an SMS corpus by combining lexical and semantic information. More precisely, our approach gives more weight to words with a sentiment (i.e. presence of words in a dedicated dictionary) for a classification task based on three classes: positive, negative, and neutral. The experiments were conducted on two corpora: an elongated SMS corpus (i.e. repetitions of characters in messages) and a non-elongated SMS corpus. We noted that non-elongated SMS were much better classified than elongated SMS. Overall, this study highlighted that the integration of semantic knowledge always improves classification.

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Specialising Paragraph Vectors for Text Polarity Detection
Fabio Tamburini

This paper presents some experiments for specialising Paragraph Vectors, a new technique for creating text fragment (phrase, sentence, paragraph, text, ...) embedding vectors, for text polarity detection. The first extension regards the injection of polarity information extracted from a polarity lexicon into embeddings and the second extension aimed at inserting word order information into Paragraph Vectors. These two extensions, when training a logistic-regression classifier on the combined embeddings, were able to produce a relevant gain in performance when compared to the standard Paragraph Vector methods proposed by Le and Mikolov (2014).

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Evaluating Lexical Similarity to build Sentiment Similarity
Grégoire Jadi | Vincent Claveau | Béatrice Daille | Laura Monceaux

In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity.

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Visualisation and Exploration of High-Dimensional Distributional Features in Lexical Semantic Classification
Maximilian Köper | Melanie Zaiß | Qi Han | Steffen Koch | Sabine Schulte im Walde

Vector space models and distributional information are widely used in NLP. The models typically rely on complex, high-dimensional objects. We present an interactive visualisation tool to explore salient lexical-semantic features of high-dimensional word objects and word similarities. Most visualisation tools provide only one low-dimensional map of the underlying data, so they are not capable of retaining the local and the global structure. We overcome this limitation by providing an additional trust-view to obtain a more realistic picture of the actual object distances. Additional tool options include the reference to a gold standard classification, the reference to a cluster analysis as well as listing the most salient (common) features for a selected subset of the words.

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SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores
Nabin Maharjan | Rajendra Banjade | Nobal Bikram Niraula | Vasile Rus

This paper introduces a ruled-based method and software tool, called SemAligner, for aligning chunks across texts in a given pair of short English texts. The tool, based on the top performing method at the Interpretable Short Text Similarity shared task at SemEval 2015, where it was used with human annotated (gold) chunks, can now additionally process plain text-pairs using two powerful chunkers we developed, e.g. using Conditional Random Fields. Besides aligning chunks, the tool automatically assigns semantic relations to the aligned chunks (such as EQUI for equivalent and OPPO for opposite) and semantic similarity scores that measure the strength of the semantic relation between the aligned chunks. Experiments show that SemAligner performs competitively for system generated chunks and that these results are also comparable to results obtained on gold chunks. SemAligner has other capabilities such as handling various input formats and chunkers as well as extending lookup resources.

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Aspectual Flexibility Increases with Agentivity and ConcretenessA Computational Classification Experiment on Polysemous Verbs
Ingrid Falk | Fabienne Martin

We present an experimental study making use of a machine learning approach to identify the factors that affect the aspectual value that characterizes verbs under each of their readings. The study is based on various morpho-syntactic and semantic features collected from a French lexical resource and on a gold standard aspectual classification of verb readings designed by an expert. Our results support the tested hypothesis, namely that agentivity and abstractness influence lexical aspect.

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mwetoolkit+sem: Integrating Word Embeddings in the mwetoolkit for Semantic MWE Processing
Silvio Cordeiro | Carlos Ramisch | Aline Villavicencio

This paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations working on distributional vectors. The compositionality score is based on the cosine distance between the MWE vector and the composition of the vectors of its member words. Our generic system can handle several types of word embeddings and MWE lists, and may combine individual word representations using several composition techniques. We evaluate our implementation on a dataset of 1042 English noun compounds, comparing different configurations of the underlying word embeddings and word-composition models. We show that our vector-based scores model non-compositionality better than standard association measures such as log-likelihood.

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Cognitively Motivated Distributional Representations of Meaning
Elias Iosif | Spiros Georgiladakis | Alexandros Potamianos

Although meaning is at the core of human cognition, state-of-the-art distributional semantic models (DSMs) are often agnostic to the findings in the area of semantic cognition. In this work, we present a novel type of DSMs motivated by the dual-processing cognitive perspective that is triggered by lexico-semantic activations in the short-term human memory. The proposed model is shown to perform better than state-of-the-art models for computing semantic similarity between words. The fusion of different types of DSMs is also investigated achieving results that are comparable or better than the state-of-the-art. The used corpora along with a set of tools, as well as large repositories of vectorial word representations are made publicly available for four languages (English, German, Italian, and Greek).

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Extending Monolingual Semantic Textual Similarity Task to Multiple Cross-lingual Settings
Yoshihiko Hayashi | Wentao Luo

This paper describes our independent effort for extending the monolingual semantic textual similarity (STS) task setting to multiple cross-lingual settings involving English, Japanese, and Chinese. So far, we have adopted a “monolingual similarity after translation” strategy to predict the semantic similarity between a pair of sentences in different languages. With this strategy, a monolingual similarity method is applied after having (one of) the target sentences translated into a pivot language. Therefore, this paper specifically details the required and developed resources to implement this framework, while presenting our current results for English-Japanese-Chinese cross-lingual STS tasks that may exemplify the validity of the framework.

We describe resources aimed at increasing the usability of the semantic representations utilized within the DELPH-IN (Deep Linguistic Processing with HPSG) consortium. We concentrate in particular on the Dependency Minimal Recursion Semantics (DMRS) formalism, a graph-based representation designed for compositional semantic representation with deep grammars. Our main focus is on English, and specifically English Resource Semantics (ERS) as used in the English Resource Grammar. We first give an introduction to ERS and DMRS and a brief overview of some existing resources and then describe in detail a new repository which has been developed to simplify the use of ERS/DMRS. We explain a number of operations on DMRS graphs which our repository supports, with sketches of the algorithms, and illustrate how these operations can be exploited in application building. We believe that this work will aid researchers to exploit the rich and effective but complex DELPH-IN resources.

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Subtask Mining from Search Query Logs for How-Knowledge Acceleration
Chung-Lun Kuo | Hsin-Hsi Chen

How-knowledge is indispensable in daily life, but has relatively less quantity and poorer quality than what-knowledge in publicly available knowledge bases. This paper first extracts task-subtask pairs from wikiHow, then mines linguistic patterns from search query logs, and finally applies the mined patterns to extract subtasks to complete given how-to tasks. To evaluate the proposed methodology, we group tasks and the corresponding recommended subtasks into pairs, and evaluate the results automatically and manually. The automatic evaluation shows the accuracy of 0.4494. We also classify the mined patterns based on prepositions and find that the prepositions like “on”, “to”, and “with” have the better performance. The results can be used to accelerate how-knowledge base construction.

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Typology of Adjectives Benchmark for Compositional Distributional Models
Daria Ryzhova | Maria Kyuseva | Denis Paperno

In this paper we present a novel application of compositional distributional semantic models (CDSMs): prediction of lexical typology. The paper introduces the notion of typological closeness, which is a novel rigorous formalization of semantic similarity based on comparison of multilingual data. Starting from the Moscow Database of Qualitative Features for adjective typology, we create four datasets of typological closeness, on which we test a range of distributional semantic models. We show that, on the one hand, vector representations of phrases based on data from one language can be used to predict how words within the phrase translate into different languages, and, on the other hand, that typological data can serve as a semantic benchmark for distributional models. We find that compositional distributional models, especially parametric ones, perform way above non-compositional alternatives on the task.

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DART: a Dataset of Arguments and their Relations on Twitter
Tom Bosc | Elena Cabrio | Serena Villata

The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.

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Port4NooJ v3.0: Integrated Linguistic Resources for Portuguese NLP
Cristina Mota | Paula Carvalho | Anabela Barreiro

This paper introduces Port4NooJ v3.0, the latest version of the Portuguese module for NooJ, highlights its main features, and details its three main new components: (i) a lexicon-grammar based dictionary of 5,177 human intransitive adjectives, and a set of local grammars that use the distributional properties of those adjectives for paraphrasing (ii) a polarity dictionary with 9,031 entries for sentiment analysis, and (iii) a set of priority dictionaries and local grammars for named entity recognition. These new components were derived and/or adapted from publicly available resources. The Port4NooJ v3.0 resource is innovative in terms of the specificity of the linguistic knowledge it incorporates. The dictionary is bilingual Portuguese-English, and the semantico-syntactic information assigned to each entry validates the linguistic relation between the terms in both languages. These characteristics, which cannot be found in any other public resource for Portuguese, make it a valuable resource for translation and paraphrasing. The paper presents the current statistics and describes the different complementary and synergic components and integration efforts.

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Collecting Language Resources for the Latvian e-Government Machine Translation Platform
Roberts Rozis | Andrejs Vasiļjevs | Raivis Skadiņš

This paper describes corpora collection activity for building large machine translation systems for Latvian e-Government platform. We describe requirements for corpora, selection and assessment of data sources, collection of the public corpora and creation of new corpora from miscellaneous sources. Methodology, tools and assessment methods are also presented along with the results achieved, challenges faced and conclusions made. Several approaches to address the data scarceness are discussed. We summarize the volume of obtained corpora and provide quality metrics of MT systems trained on this data. Resulting MT systems for English-Latvian, Latvian English and Latvian Russian are integrated in the Latvian e-service portal and are freely available on website HUGO.LV. This paper can serve as a guidance for similar activities initiated in other countries, particularly in the context of European Language Resource Coordination action.

The Nederlab project aims to bring together all digitized texts relevant to the Dutch national heritage, the history of the Dutch language and culture (circa 800 – present) in one user friendly and tool enriched open access web interface. This paper describes Nederlab halfway through the project period and discusses the collections incorporated, back-office processes, system back-end as well as the Nederlab Research Portal end-user web application.

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A Semi-Supervised Approach for Gender Identification
Juan Soler | Leo Wanner

In most of the research studies on Author Profiling, large quantities of correctly labeled data are used to train the models. However, this does not reflect the reality in forensic scenarios: in practical linguistic forensic investigations, the resources that are available to profile the author of a text are usually scarce. To pay tribute to this fact, we implemented a Semi-Supervised Learning variant of the k nearest neighbors algorithm that uses small sets of labeled data and a larger amount of unlabeled data to classify the authors of texts by gender (man vs woman). We describe the enriched KNN algorithm and show that the use of unlabeled instances improves the accuracy of our gender identification model. We also present a feature set that facilitates the use of a very small number of instances, reaching accuracies higher than 70% with only 113 instances to train the model. It is also shown that the algorithm also performs well using publicly available data.

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Ensemble Classification of Grants using LDA-based Features
Yannis Korkontzelos | Beverley Thomas | Makoto Miwa | Sophia Ananiadou

Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision. It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments.

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Edit Categories and Editor Role Identification in Wikipedia
Diyi Yang | Aaron Halfaker | Robert Kraut | Eduard Hovy

In this work, we introduced a corpus for categorizing edit types in Wikipedia. This fine-grained taxonomy of edit types enables us to differentiate editing actions and find editor roles in Wikipedia based on their low-level edit types. To do this, we first created an annotated corpus based on 1,996 edits obtained from 953 article revisions and built machine-learning models to automatically identify the edit categories associated with edits. Building on this automated measurement of edit types, we then applied a graphical model analogous to Latent Dirichlet Allocation to uncover the latent roles in editors’ edit histories. Applying this technique revealed eight different roles editors play, such as Social Networker, Substantive Expert, etc.

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Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic
Faisal Al-Shargi | Aidan Kaplan | Ramy Eskander | Nizar Habash | Owen Rambow

We present new language resources for Moroccan and Sanaani Yemeni Arabic. The resources include corpora for each dialect which have been morphologically annotated, and morphological analyzers for each dialect which are derived from these corpora. These are the first sets of resources for Moroccan and Yemeni Arabic. The resources will be made available to the public.

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Merging Data Resources for Inflectional and Derivational Morphology in Czech
Zdeněk Žabokrtský | Magda Ševčíková | Milan Straka | Jonáš Vidra | Adéla Limburská

The paper deals with merging two complementary resources of morphological data previously existing for Czech, namely the inflectional dictionary MorfFlex CZ and the recently developed lexical network DeriNet. The MorfFlex CZ dictionary has been used by a morphological analyzer capable of analyzing/generating several million Czech word forms according to the rules of Czech inflection. The DeriNet network contains several hundred thousand Czech lemmas interconnected with links corresponding to derivational relations (relations between base words and words derived from them). After summarizing basic characteristics of both resources, the process of merging is described, focusing on both rather technical aspects (growth of the data, measuring the quality of newly added derivational relations) and linguistic issues (treating lexical homonymy and vowel/consonant alternations). The resulting resource contains 970 thousand lemmas connected with 715 thousand derivational relations and is publicly available on the web under the CC-BY-NC-SA license. The data were incorporated in the MorphoDiTa library version 2.0 (which provides morphological analysis, generation, tagging and lemmatization for Czech) and can be browsed and searched by two web tools (DeriNet Viewer and DeriNet Search tool).

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A New Integrated Open-source Morphological Analyzer for Hungarian
Attila Novák | Borbála Siklósi | Csaba Oravecz

The goal of a Hungarian research project has been to create an integrated Hungarian natural language processing framework. This infrastructure includes tools for analyzing Hungarian texts, integrated into a standardized environment. The morphological analyzer is one of the core components of the framework. The goal of this paper is to describe a fast and customizable morphological analyzer and its development framework, which synthesizes and further enriches the morphological knowledge implemented in previous tools existing for Hungarian. In addition, we present the method we applied to add semantic knowledge to the lexical database of the morphology. The method utilizes neural word embedding models and morphological and shallow syntactic knowledge.

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New release of Mixer-6: Improved validity for phonetic study of speaker variation and identification
Eleanor Chodroff | Matthew Maciejewski | Jan Trmal | Sanjeev Khudanpur | John Godfrey

The Mixer series of speech corpora were collected over several years, principally to support annual NIST evaluations of speaker recognition (SR) technologies. These evaluations focused on conversational speech over a variety of channels and recording conditions. One of the series, Mixer-6, added a new condition, read speech, to support basic scientific research on speaker characteristics, as well as technology evaluation. With read speech it is possible to make relatively precise measurements of phonetic events and features, which can be correlated with the performance of speaker recognition algorithms, or directly used in phonetic analysis of speaker variability. The read speech, as originally recorded, was adequate for large-scale evaluations (e.g., fixed-text speaker ID algorithms) but only marginally suitable for acoustic-phonetic studies. Numerous errors due largely to speaker behavior remained in the corpus, with no record of their locations or rate of occurrence. We undertook the effort to correct this situation with automatic methods supplemented by human listening and annotation. The present paper describes the tools and methods, resulting corrections, and some examples of the kinds of research studies enabled by these enhancements.

In this paper, we describe a new database with audio recordings of non-native (L2) speakers of English, and the perceptual evaluation experiment conducted with native English speakers for assessing the prosody of each recording. These annotations are then used to compute the gold standard using different methods, and a series of regression experiments is conducted to evaluate their impact on the performance of a regression model predicting the degree of naturalness of L2 speech. Further, we compare the relevance of different feature groups modelling prosody in general (without speech tempo), speech rate and pauses modelling speech tempo (fluency), voice quality, and a variety of spectral features. We also discuss the impact of various fusion strategies on performance. Overall, our results demonstrate that the prosody of non-native speakers of English as L2 can be reliably assessed using supra-segmental audio features; prosodic features seem to be the most important ones.

The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning. The motivation for setting up this corpus was that there is no phonetically annotated and segmented corpus for this language pair of comparable of size and coverage. In contrast to most learner corpora, the IFCASL corpus incorporate data for a language pair in both directions, i.e. in our case French learners of German, and German learners of French. In addition, the corpus is complemented by two sub-corpora of native speech by the same speakers. The corpus provides spoken data by about 100 speakers with comparable productions, annotated and segmented on the word and the phone level, with more than 50% manually corrected data. The paper reports on inter-annotator agreement and the optimization of the acoustic models for forced speech-text alignment in exercises for computer-assisted pronunciation training. Example studies based on the corpus data with a phonetic focus include topics such as the realization of /h/ and glottal stop, final devoicing of obstruents, vowel quantity and quality, pitch range, and tempo.

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LELIO: An Auto-Adaptative System to Acquire Domain Lexical Knowledge in Technical Texts
Patrick Saint-Dizier

In this paper, we investigate some language acquisition facets of an auto-adaptative system that can automatically acquire most of the relevant lexical knowledge and authoring practices for an application in a given domain. This is the LELIO project: producing customized LELIE solutions. Our goal, within the framework of LELIE (a system that tags language uses that do not follow the Constrained Natural Language principles), is to automate the long, costly and error prone lexical customization of LELIE to a given application domain. Technical texts being relatively restricted in terms of syntax and lexicon, results obtained show that this approach is feasible and relatively reliable. By auto-adaptative, we mean that the system learns from a sample of the application corpus the various lexical terms and uses crucial for LELIE to work properly (e.g. verb uses, fuzzy terms, business terms, stylistic patterns). A technical writer validation method is developed at each step of the acquisition.

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Wikification for Scriptio Continua
Yugo Murawaki | Shinsuke Mori

The fact that Japanese employs scriptio continua, or a writing system without spaces, complicates the first step of an NLP pipeline. Word segmentation is widely used in Japanese language processing, and lexical knowledge is crucial for reliable identification of words in text. Although external lexical resources like Wikipedia are potentially useful, segmentation mismatch prevents them from being straightforwardly incorporated into the word segmentation task. If we intentionally violate segmentation standards with the direct incorporation, quantitative evaluation will be no longer feasible. To address this problem, we propose to define a separate task that directly links given texts to an external resource, that is, wikification in the case of Wikipedia. By doing so, we can circumvent segmentation mismatch that may not necessarily be important for downstream applications. As the first step to realize the idea, we design the task of Japanese wikification and construct wikification corpora. We annotated subsets of the Balanced Corpus of Contemporary Written Japanese plus Twitter short messages. We also implement a simple wikifier and investigate its performance on these corpora.

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Accessing and Elaborating Walenty - a Valence Dictionary of Polish - via Internet Browser
Bartłomiej Nitoń | Tomasz Bartosiak | Elżbieta Hajnicz

This article presents Walenty - a new valence dictionary of Polish predicates, concentrating on its creation process and access via Internet browser. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. The semantic layer shows how predicates and their arguments are involved in a situation described in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Walenty also contains a powerful phraseological (idiomatic) component. Walenty has been created and can be accessed remotely with a dedicated tool called Slowal. In this article, we focus on most important functionalities of this system. First, we will depict how to access the dictionary and how built-in filtering system (covering both syntactic and semantic phenomena) works. Later, we will describe the process of creating dictionary by Slowal tool that both supports and controls the work of lexicographers.

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CEPLEXicon ― A Lexicon of Child European Portuguese
Ana Lúcia Santos | Maria João Freitas | Aida Cardoso

CEPLEXicon (version 1.1) is a child lexicon resulting from the automatic tagging of two child corpora: the corpus Santos (Santos, 2006; Santos et al. 2014) and the corpus Child ― Adult Interaction (Freitas et al. 2012), which integrates information from the corpus Freitas (Freitas, 1997). This lexicon includes spontaneous speech produced by seven children (1;02.00 to 3;11.12) during approximately 86h of child-adult interaction. The automatic tagging comprised the lemmatization and morphosyntactic classification of the speech produced by the seven children included in the two child corpora; the lexicon contains information pertaining to lemmas and syntactic categories as well as absolute number of occurrences and frequencies in three age intervals: < 2 years; ≥ 2 years and < 3 years; ≥ 3 years. The information included in this lexicon and the format in which it is presented enables research in different areas and allows researchers to obtain measures of lexical growth. CEPLEXicon is available through the ELRA catalogue.

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Extracting Weighted Language Lexicons from Wikipedia
Gregory Grefenstette

Language models are used in applications as diverse as speech recognition, optical character recognition and information retrieval. They are used to predict word appearance, and to weight the importance of words in these applications. One basic element of language models is the list of words in a language. Another is the unigram frequency of each word. But this basic information is not available for most languages in the world. Since the multilingual Wikipedia project encourages the production of encyclopedic-like articles in many world languages, we can find there an ever-growing source of text from which to extract these two language modelling elements: word list and frequency. Here we present a simple technique for converting this Wikipedia text into lexicons of weighted unigrams for the more than 280 languages present currently present in Wikipedia. The lexicons produced, and the source code for producing them in a Linux-based system are here made available for free on the Web.

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Wiktionnaire’s Wikicode GLAWIfied: a Workable French Machine-Readable Dictionary
Nabil Hathout | Franck Sajous

GLAWI is a free, large-scale and versatile Machine-Readable Dictionary (MRD) that has been extracted from the French language edition of Wiktionary, called Wiktionnaire. In (Sajous and Hathout, 2015), we introduced GLAWI, gave the rationale behind the creation of this lexicographic resource and described the extraction process, focusing on the conversion and standardization of the heterogeneous data provided by this collaborative dictionary. In the current article, we describe the content of GLAWI and illustrate how it is structured. We also suggest various applications, ranging from linguistic studies, NLP applications to psycholinguistic experimentation. They all can take advantage of the diversity of the lexical knowledge available in GLAWI. Besides this diversity and extensive lexical coverage, GLAWI is also remarkable because it is the only free lexical resource of contemporary French that contains definitions. This unique material opens way to the renewal of MRD-based methods, notably the automated extraction and acquisition of semantic relations.

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A Corpus of Images and Text in Online News
Laura Hollink | Adriatik Bedjeti | Martin van Harmelen | Desmond Elliott

In recent years, several datasets have been released that include images and text, giving impulse to new methods that combine natural language processing and computer vision. However, there is a need for datasets of images in their natural textual context. The ION corpus contains 300K news articles published between August 2014 - 2015 in five online newspapers from two countries. The 1-year coverage over multiple publishers ensures a broad scope in terms of topics, image quality and editorial viewpoints. The corpus consists of JSON-LD files with the following data about each article: the original URL of the article on the news publisher’s website, the date of publication, the headline of the article, the URL of the image displayed with the article (if any), and the caption of that image. Neither the article text nor the images themselves are included in the corpus. Instead, the images are distributed as high-dimensional feature vectors extracted from a Convolutional Neural Network, anticipating their use in computer vision tasks. The article text is represented as a list of automatically generated entity and topic annotations in the form of Wikipedia/DBpedia pages. This facilitates the selection of subsets of the corpus for separate analysis or evaluation.

There are as many sign languages as there are deaf communities in the world. Linguists have been collecting corpora of different sign languages and annotating them extensively in order to study and understand their properties. On the other hand, the field of computer vision has approached the sign language recognition problem as a grand challenge and research efforts have intensified in the last 20 years. However, corpora collected for studying linguistic properties are often not suitable for sign language recognition as the statistical methods used in the field require large amounts of data. Recently, with the availability of inexpensive depth cameras, groups from the computer vision community have started collecting corpora with large number of repetitions for sign language recognition research. In this paper, we present the BosphorusSign Turkish Sign Language corpus, which consists of 855 sign and phrase samples from the health, finance and everyday life domains. The corpus is collected using the state-of-the-art Microsoft Kinect v2 depth sensor, and will be the first in this sign language research field. Furthermore, there will be annotations rendered by linguists so that the corpus will appeal both to the linguistic and sign language recognition research communities.

Ambient Assisted Living aims at enhancing the quality of life of older and disabled people at home thanks to Smart Homes. In particular, regarding elderly living alone at home, the detection of distress situation after a fall is very important to reassure this kind of population. However, many studies do not include tests in real settings, because data collection in this domain is very expensive and challenging and because of the few available data sets. The C IRDO corpus is a dataset recorded in realistic conditions in D OMUS , a fully equipped Smart Home with microphones and home automation sensors, in which participants performed scenarios including real falls on a carpet and calls for help. These scenarios were elaborated thanks to a field study involving elderly persons. Experiments related in a first part to distress detection in real-time using audio and speech analysis and in a second part to fall detection using video analysis are presented. Results show the difficulty of the task. The database can be used as standardized database by researchers to evaluate and compare their systems for elderly person’s assistance.

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Semi-automatically Alignment of Predicates between Speech and OntoNotes data
Niraj Shrestha | Marie-Francine Moens

Speech data currently receives a growing attention and is an important source of information. We still lack suitable corpora of transcribed speech annotated with semantic roles that can be used for semantic role labeling (SRL), which is not the case for written data. Semantic role labeling in speech data is a challenging and complex task due to the lack of sentence boundaries and the many transcription errors such as insertion, deletion and misspellings of words. In written data, SRL evaluation is performed at the sentence level, but in speech data sentence boundaries identification is still a bottleneck which makes evaluation more complex. In this work, we semi-automatically align the predicates found in transcribed speech obtained with an automatic speech recognizer (ASR) with the predicates found in the corresponding written documents of the OntoNotes corpus and manually align the semantic roles of these predicates thus obtaining annotated semantic frames in the speech data. This data can serve as gold standard alignments for future research in semantic role labeling of speech data.

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CORILSE: a Spanish Sign Language Repository for Linguistic Analysis
María del Carmen Cabeza-Pereiro | José Mª Garcia-Miguel | Carmen García Mateo | José Luis Alba Castro

CORILSE is a computerized corpus of Spanish Sign Language (Lengua de Signos Española, LSE). It consists of a set of recordings from different discourse genres by Galician signers living in the city of Vigo. In this paper we describe its annotation system, developed on the basis of pre-existing ones (mostly the model of Auslan corpus). This includes primary annotation of id-glosses for manual signs, annotation of non-manual component, and secondary annotation of grammatical categories and relations, because this corpus is been built for grammatical analysis, in particular argument structures in LSE. Up until this moment the annotation has been basically made by hand, which is a slow and time-consuming task. The need to facilitate this process leads us to engage in the development of automatic or semi-automatic tools for manual and facial recognition. Finally, we also present the web repository that will make the corpus available to different types of users, and will allow its exploitation for research purposes and other applications (e.g. teaching of LSE or design of tasks for signed language assessment).

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The OFAI Multi-Modal Task Description Corpus
Stephanie Schreitter | Brigitte Krenn

The OFAI Multimodal Task Description Corpus (OFAI-MMTD Corpus) is a collection of dyadic teacher-learner (human-human and human-robot) interactions. The corpus is multimodal and tracks the communication signals exchanged between interlocutors in task-oriented scenarios including speech, gaze and gestures. The focus of interest lies on the communicative signals conveyed by the teacher and which objects are salient at which time. Data are collected from four different task description setups which involve spatial utterances, navigation instructions and more complex descriptions of joint tasks.

In recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search by natural language queries. In order to concentrate on language ambiguities, we propose to use a well-defined “real world,” that is game states. We built a corpus consisting of pairs of sentences and a game state. The game we focus on is shogi (Japanese chess). We collected 742,286 commentary sentences in Japanese. They are spontaneously generated contrary to natural language annotations in many image datasets provided by human workers on Amazon Mechanical Turk. We defined domain specific named entities and we segmented 2,508 sentences into words manually and annotated each word with a named entity tag. We describe a detailed definition of named entities and show some statistics of our game commentary corpus. We also show the results of the experiments of word segmentation and named entity recognition. The accuracies are as high as those on general domain texts indicating that we are ready to tackle various new problems related to the real world.

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.

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Finding Recurrent Features of Image Schema Gestures: the FIGURE corpus
Andy Luecking | Alexander Mehler | Désirée Walther | Marcel Mauri | Dennis Kurfürst

The Frankfurt Image GestURE corpus (FIGURE) is introduced. The corpus data is collected in an experimental setting where 50 naive participants spontaneously produced gestures in response to five to six terms from a total of 27 stimulus terms. The stimulus terms have been compiled mainly from image schemata from psycholinguistics, since such schemata provide a panoply of abstract contents derived from natural language use. The gestures have been annotated for kinetic features. FIGURE aims at finding (sets of) stable kinetic feature configurations associated with the stimulus terms. Given such configurations, they can be used for designing HCI gestures that go beyond pre-defined gesture vocabularies or touchpad gestures. It is found, for instance, that movement trajectories are far more informative than handshapes, speaking against purely handshape-based HCI vocabularies. Furthermore, the mean temporal duration of hand and arm movements associated vary with the stimulus terms, indicating a dynamic dimension not covered by vocabulary-based approaches. Descriptive results are presented and related to findings from gesture studies and natural language dialogue.

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An Interaction-Centric Dataset for Learning Automation Rules in Smart Homes
Kai Frederic Engelmann | Patrick Holthaus | Britta Wrede | Sebastian Wrede

The term smart home refers to a living environment that by its connected sensors and actuators is capable of providing intelligent and contextualised support to its user. This may result in automated behaviors that blends into the user’s daily life. However, currently most smart homes do not provide such intelligent support. A first step towards such intelligent capabilities lies in learning automation rules by observing the user’s behavior. We present a new type of corpus for learning such rules from user behavior as observed from the events in a smart homes sensor and actuator network. The data contains information about intended tasks by the users and synchronized events from this network. It is derived from interactions of 59 users with the smart home in order to solve five tasks. The corpus contains recordings of more than 40 different types of data streams and has been segmented and pre-processed to increase signal quality. Overall, the data shows a high noise level on specific data types that can be filtered out by a simple smoothing approach. The resulting data provides insights into event patterns resulting from task specific user behavior and thus constitutes a basis for machine learning approaches to learn automation rules.

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A Web Tool for Building Parallel Corpora of Spoken and Sign Languages
Alex Becker | Fabio Kepler | Sara Candeias

In this paper we describe our work in building an online tool for manually annotating texts in any spoken language with SignWriting in any sign language. The existence of such tool will allow the creation of parallel corpora between spoken and sign languages that can be used to bootstrap the creation of efficient tools for the Deaf community. As an example, a parallel corpus between English and American Sign Language could be used for training Machine Learning models for automatic translation between the two languages. Clearly, this kind of tool must be designed in a way that it eases the task of human annotators, not only by being easy to use, but also by giving smart suggestions as the annotation progresses, in order to save time and effort. By building a collaborative, online, easy to use annotation tool for building parallel corpora between spoken and sign languages we aim at helping the development of proper resources for sign languages that can then be used in state-of-the-art models currently used in tools for spoken languages. There are several issues and difficulties in creating this kind of resource, and our presented tool already deals with some of them, like adequate text representation of a sign and many to many alignments between words and signs.

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Issues and Challenges in Annotating Urdu Action Verbs on the IMAGACT4ALL Platform
Sharmin Muzaffar | Pitambar Behera | Girish Jha

In South-Asian languages such as Hindi and Urdu, action verbs having compound constructions and serial verbs constructions pose serious problems for natural language processing and other linguistic tasks. Urdu is an Indo-Aryan language spoken by 51, 500, 0001 speakers in India. Action verbs that occur spontaneously in day-to-day communication are highly ambiguous in nature semantically and as a consequence cause disambiguation issues that are relevant and applicable to Language Technologies (LT) like Machine Translation (MT) and Natural Language Processing (NLP). IMAGACT4ALL is an ontology-driven web-based platform developed by the University of Florence for storing action verbs and their inter-relations. This group is currently collaborating with Jawaharlal Nehru University (JNU) in India to connect Indian languages on this platform. Action verbs are frequently used in both written and spoken discourses and refer to various meanings because of their polysemic nature. The IMAGACT4ALL platform stores each 3d animation image, each one of them referring to a variety of possible ontological types, which in turn makes the annotation task for the annotator quite challenging with regard to selecting verb argument structure having a range of probability distribution. The authors, in this paper, discuss the issues and challenges such as complex predicates (compound and conjunct verbs), ambiguously animated video illustrations, semantic discrepancies, and the factors of verb-selection preferences that have produced significant problems in annotating Urdu verbs on the IMAGACT ontology.

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Domain Ontology Learning Enhanced by Optimized Relation Instance in DBpedia
Liumingjing Xiao | Chong Ruan | An Yang | Junhao Zhang | Junfeng Hu

Ontologies are powerful to support semantic based applications and intelligent systems. While ontology learning are challenging due to its bottleneck in handcrafting structured knowledge sources and training data. To address this difficulty, many researchers turn to ontology enrichment and population using external knowledge sources such as DBpedia. In this paper, we propose a method using DBpedia in a different manner. We utilize relation instances in DBpedia to supervise the ontology learning procedure from unstructured text, rather than populate the ontology structure as a post-processing step. We construct three language resources in areas of computer science: enriched Wikipedia concept tree, domain ontology, and gold standard from NSFC taxonomy. Experiment shows that the result of ontology learning from corpus of computer science can be improved via the relation instances extracted from DBpedia in the same field. Furthermore, making distinction between the relation instances and applying a proper weighting scheme in the learning procedure lead to even better result.

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Constructing a Norwegian Academic Wordlist
Janne M Johannessen | Arash Saidi | Kristin Hagen

We present the development of a Norwegian Academic Wordlist (AKA list) for the Norwegian Bokmäl variety. To identify specific academic vocabulary we developed a 100-million-word academic corpus based on the University of Oslo archive of digital publications. Other corpora were used for testing and developing general word lists. We tried two different methods, those of Carlund et al. (2012) and Gardner & Davies (2013), and compared them. The resulting list is presented on a web site, where the words can be inspected in different ways, and freely downloaded.

This paper presents the Event and Implied Situation Ontology (ESO), a manually constructed resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology is built on top of existing resources such as WordNet, SUMO and FrameNet. The ontology is injected to the Predicate Matrix, a resource that integrates predicate and role information from amongst others FrameNet, VerbNet, PropBank, NomBank and WordNet. We illustrate how these resources are used on large document collections to detect information that otherwise would have remained implicit. The ontology is evaluated on two aspects: recall and precision based on a manually annotated corpus and secondly, on the quality of the knowledge inferred by the situation assertions in the ontology. Evaluation results on the quality of the system show that 50% of the events typed and enriched with ESO assertions are correct.

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Combining Ontologies and Neural Networks for Analyzing Historical Language Varieties. A Case Study in Middle Low German
Maria Sukhareva | Christian Chiarcos

In this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during the Middle Ages and a dominant language around the Baltic Sea by the time. To our best knowledge, this is the first experiment in automatically producing morphosyntactic annotations for Middle Low German, and accordingly, no part-of-speech (POS) tagset is currently agreed upon. In our experiment, we illustrate how ontology-based specifications of projected annotations can be employed to circumvent this issue: Instead of training and evaluating against a given tagset, we decomponse it into independent features which are predicted independently by a neural network. Using consistency constraints (axioms) from an ontology, then, the predicted feature probabilities are decoded into a sound ontological representation. Using these representations, we can finally bootstrap a POS tagset capturing only morphosyntactic features which could be reliably predicted. In this way, our approach is capable to optimize precision and recall of morphosyntactic annotations simultaneously with bootstrapping a tagset rather than performing iterative cycles.

As part of a human-robot interaction project, we are interested by gestural modality as one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot. Our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context. During the experiment, the subject has to use non-verbal cues (i.e. gestures) to interact with a robot that is the referee. The subject is unaware that his gestures will be the focus of our study. In the second part of the experiment, we asked the subjects to do the gestures he had produced in the experiment, those are the explicit gestures. The implicit gestures are compared with explicitly produced ones to determine a relevant ontology. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the “socio-affective glue” dynamics.

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A Taxonomy of Spanish Nouns, a Statistical Algorithm to Generate it and its Implementation in Open Source Code
Rogelio Nazar | Irene Renau

In this paper we describe our work in progress in the automatic development of a taxonomy of Spanish nouns, we offer the Perl implementation we have so far, and we discuss the different problems that still need to be addressed. We designed a statistically-based taxonomy induction algorithm consisting of a combination of different strategies not involving explicit linguistic knowledge. Being all quantitative, the strategies we present are however of different nature. Some of them are based on the computation of distributional similarity coefficients which identify pairs of sibling words or co-hyponyms, while others are based on asymmetric co-occurrence and identify pairs of parent-child words or hypernym-hyponym relations. A decision making process is then applied to combine the results of the previous steps, and finally connect lexical units to a basic structure containing the most general categories of the language. We evaluate the quality of the taxonomy both manually and also using Spanish Wordnet as a gold-standard. We estimate an average of 89.07% precision and 25.49% recall considering only the results which the algorithm presents with high degree of certainty, or 77.86% precision and 33.72% recall considering all results.

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FOLK-Gold ― A Gold Standard for Part-of-Speech-Tagging of Spoken German
Swantje Westpfahl | Thomas Schmidt

In this paper, we present a GOLD standard of part-of-speech tagged transcripts of spoken German. The GOLD standard data consists of four annotation layers ― transcription (modified orthography), normalization (standard orthography), lemmatization and POS tags ― all of which have undergone careful manual quality control. It comes with guidelines for the manual POS annotation of transcripts of German spoken data and an extended version of the STTS (Stuttgart Tübingen Tagset) which accounts for phenomena typically found in spontaneous spoken German. The GOLD standard was developed on the basis of the Research and Teaching Corpus of Spoken German, FOLK, and is, to our knowledge, the first such dataset based on a wide variety of spontaneous and authentic interaction types. It can be used as a basis for further development of language technology and corpus linguistic applications for German spoken language.

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Fast and Robust POS tagger for Arabic Tweets Using Agreement-based Bootstrapping
Fahad Albogamy | Allan Ramsay

Part-of-Speech(POS) tagging is a key step in many NLP algorithms. However, tweets are difficult to POS tag because they are short, are not always written maintaining formal grammar and proper spelling, and abbreviations are often used to overcome their restricted lengths. Arabic tweets also show a further range of linguistic phenomena such as usage of different dialects, romanised Arabic and borrowing foreign words. In this paper, we present an evaluation and a detailed error analysis of state-of-the-art POS taggers for Arabic when applied to Arabic tweets. On the basis of this analysis, we combine normalisation and external knowledge to handle the domain noisiness and exploit bootstrapping to construct extra training data in order to improve POS tagging for Arabic tweets. Our results show significant improvements over the performance of a number of well-known taggers for Arabic.

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Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art
Steffen Eger | Rüdiger Gleim | Alexander Mehler

This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementations for POS tagging. We examine to what degree recent efforts in tagger development are reflected by improved accuracies ― and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-domain evaluation. Out-domain evaluations are particularly insightful because the distribution of the data which is being tagged by a user will typically differ from the distribution on which the tagger has been trained. Furthermore, two lemmatization techniques are evaluated. Finally, we compare pipeline tagging vs. a tagging approach that acknowledges dependencies between inflectional categories.

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TLT-CRF: A Lexicon-supported Morphological Tagger for Latin Based on Conditional Random Fields
Tim vor der Brück | Alexander Mehler

We present a morphological tagger for Latin, called TTLab Latin Tagger based on Conditional Random Fields (TLT-CRF) which uses a large Latin lexicon. Beyond Part of Speech (PoS), TLT-CRF tags eight inflectional categories of verbs, adjectives or nouns. It utilizes a statistical model based on CRFs together with a rule interpreter that addresses scenarios of sparse training data. We present results of evaluating TLT-CRF to answer the question what can be learnt following the paradigm of 1st order CRFs in conjunction with a large lexical resource and a rule interpreter. Furthermore, we investigate the contigency of representational features and targeted parts of speech to learn about selective features.

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Cross-lingual and Supervised Models for Morphosyntactic Annotation: a Comparison on Romanian
Lauriane Aufrant | Guillaume Wisniewski | François Yvon

Because of the small size of Romanian corpora, the performance of a PoS tagger or a dependency parser trained with the standard supervised methods fall far short from the performance achieved in most languages. That is why, we apply state-of-the-art methods for cross-lingual transfer on Romanian tagging and parsing, from English and several Romance languages. We compare the performance with monolingual systems trained with sets of different sizes and establish that training on a few sentences in target language yields better results than transferring from large datasets in other languages.

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Corpus vs. Lexicon Supervision in Morphosyntactic Tagging: the Case of Slovene
Nikola Ljubešić | Tomaž Erjavec

In this paper we present a tagger developed for inflectionally rich languages for which both a training corpus and a lexicon are available. We do not constrain the tagger by the lexicon entries, allowing both for lexicon incompleteness and noisiness. By using the lexicon indirectly through features we allow for known and unknown words to be tagged in the same manner. We test our tagger on Slovene data, obtaining a 25% error reduction of the best previous results both on known and unknown words. Given that Slovene is, in comparison to some other Slavic languages, a well-resourced language, we perform experiments on the impact of token (corpus) vs. type (lexicon) supervision, obtaining useful insights in how to balance the effort of extending resources to yield better tagging results.

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Challenges and Solutions for Consistent Annotation of Vietnamese Treebank
Quy Nguyen | Yusuke Miyao | Ha Le | Ngan Nguyen

Treebanks are important resources for researchers in natural language processing, speech recognition, theoretical linguistics, etc. To strengthen the automatic processing of the Vietnamese language, a Vietnamese treebank has been built. However, the quality of this treebank is not satisfactory and is a possible source for the low performance of Vietnamese language processing. We have been building a new treebank for Vietnamese with about 40,000 sentences annotated with three layers: word segmentation, part-of-speech tagging, and bracketing. In this paper, we describe several challenges of Vietnamese language and how we solve them in developing annotation guidelines. We also present our methods to improve the quality of the annotation guidelines and ensure annotation accuracy and consistency. Experiment results show that inter-annotator agreement ratios and accuracy are higher than 90% which is satisfactory.

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Correcting Errors in a Treebank Based on Tree Mining
Kanta Suzuki | Yoshihide Kato | Shigeki Matsubara

This paper provides a new method to correct annotation errors in a treebank. The previous error correction method constructs a pseudo parallel corpus where incorrect partial parse trees are paired with correct ones, and extracts error correction rules from the parallel corpus. By applying these rules to a treebank, the method corrects errors. However, this method does not achieve wide coverage of error correction. To achieve wide coverage, our method adopts a different approach. In our method, we consider that an infrequent pattern which can be transformed to a frequent one is an annotation error pattern. Based on a tree mining technique, our method seeks such infrequent tree patterns, and constructs error correction rules each of which consists of an infrequent pattern and a corresponding frequent pattern. We conducted an experiment using the Penn Treebank. We obtained 1,987 rules which are not constructed by the previous method, and the rules achieved good precision.

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4Couv: A New Treebank for French
Philippe Blache | Grégoire de Montcheuil | Laurent Prévot | Stéphane Rauzy

The question of the type of text used as primary data in treebanks is of certain importance. First, it has an influence at the discourse level: an article is not organized in the same way as a novel or a technical document. Moreover, it also has consequences in terms of semantic interpretation: some types of texts can be easier to interpret than others. We present in this paper a new type of treebank which presents the particularity to answer to specific needs of experimental linguistic. It is made of short texts (book backcovers) that presents a strong coherence in their organization and can be rapidly interpreted. This type of text is adapted to short reading sessions, making it easy to acquire physiological data (e.g. eye movement, electroencepholagraphy). Such a resource offers reliable data when looking for correlations between computational models and human language processing.

This paper presents a new linguistic resource for the study and computational processing of Portuguese. CINTIL DependencyBank PREMIUM is a corpus of Portuguese news text, accurately manually annotated with a wide range of linguistic information (morpho-syntax, named-entities, syntactic function and semantic roles), making it an invaluable resource specially for the development and evaluation of data-driven natural language processing tools. The corpus is under active development, reaching 4,000 sentences in its current version. The paper also reports on the training and evaluation of a dependency parser over this corpus. CINTIL DependencyBank PREMIUM is freely-available for research purposes through META-SHARE.

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Estonian Dependency Treebank: from Constraint Grammar tagset to Universal Dependencies
Kadri Muischnek | Kaili Müürisep | Tiina Puolakainen

This paper presents the first version of Estonian Universal Dependencies Treebank which has been semi-automatically acquired from Estonian Dependency Treebank and comprises ca 400,000 words (ca 30,000 sentences) representing the genres of fiction, newspapers and scientific writing. Article analyses the differences between two annotation schemes and the conversion procedure to Universal Dependencies format. The conversion has been conducted by manually created Constraint Grammar transfer rules. As the rules enable to consider unbounded context, include lexical information and both flat and tree structure features at the same time, the method has proved to be reliable and flexible enough to handle most of transformations. The automatic conversion procedure achieved LAS 95.2%, UAS 96.3% and LA 98.4%. If punctuation marks were excluded from the calculations, we observed LAS 96.4%, UAS 97.7% and LA 98.2%. Still the refinement of the guidelines and methodology is needed in order to re-annotate some syntactic phenomena, e.g. inter-clausal relations. Although automatic rules usually make quite a good guess even in obscure conditions, some relations should be checked and annotated manually after the main conversion.

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The Universal Dependencies Treebank of Spoken Slovenian
Kaja Dobrovoljc | Joakim Nivre

This paper presents the construction of an open-source dependency treebank of spoken Slovenian, the first syntactically annotated collection of spontaneous speech in Slovenian. The treebank has been manually annotated using the Universal Dependencies annotation scheme, a one-layer syntactic annotation scheme with a high degree of cross-modality, cross-framework and cross-language interoperability. In this original application of the scheme to spoken language transcripts, we address a wide spectrum of syntactic particularities in speech, either by extending the scope of application of existing universal labels or by proposing new speech-specific extensions. The initial analysis of the resulting treebank and its comparison with the written Slovenian UD treebank confirms significant syntactic differences between the two language modalities, with spoken data consisting of shorter and more elliptic sentences, less and simpler nominal phrases, and more relations marking disfluencies, interaction, deixis and modality.

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Introducing the Asian Language Treebank (ALT)
Ye Kyaw Thu | Win Pa Pa | Masao Utiyama | Andrew Finch | Eiichiro Sumita

This paper introduces the ALT project initiated by the Advanced Speech Translation Research and Development Promotion Center (ASTREC), NICT, Kyoto, Japan. The aim of this project is to accelerate NLP research for Asian languages such as Indonesian, Japanese, Khmer, Laos, Malay, Myanmar, Philippine, Thai and Vietnamese. The original resource for this project was English articles that were randomly selected from Wikinews. The project has so far created a corpus for Myanmar and will extend in scope to include other languages in the near future. A 20000-sentence corpus of Myanmar that has been manually translated from an English corpus has been word segmented, word aligned, part-of-speech tagged and constituency parsed by human annotators. In this paper, we present the implementation steps for creating the treebank in detail, including a description of the ALT web-based treebanking tool. Moreover, we report statistics on the annotation quality of the Myanmar treebank created so far.

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Universal Dependencies for Norwegian
Lilja Øvrelid | Petter Hohle

This article describes the conversion of the Norwegian Dependency Treebank to the Universal Dependencies scheme. This paper details the mapping of PoS tags, morphological features and dependency relations and provides a description of the structural changes made to NDT analyses in order to make it compliant with the UD guidelines. We further present PoS tagging and dependency parsing experiments which report first results for the processing of the converted treebank. The full converted treebank was made available with the 1.2 release of the UD treebanks.

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Fostering the Next Generation of European Language Technology: Recent Developments ― Emerging Initiatives ― Challenges and Opportunities
Georg Rehm | Jan Hajič | Josef van Genabith | Andrejs Vasiljevs

META-NET is a European network of excellence, founded in 2010, that consists of 60 research centres in 34 European countries. One of the key visions and goals of META-NET is a truly multilingual Europe, which is substantially supported and realised through language technologies. In this article we provide an overview of recent developments around the multilingual Europe topic, we also describe recent and upcoming events as well as recent and upcoming strategy papers. Furthermore, we provide overviews of two new emerging initiatives, the CEF.AT and ELRC activity on the one hand and the Cracking the Language Barrier federation on the other. The paper closes with several suggested next steps in order to address the current challenges and to open up new opportunities.

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Yes, We Care! Results of the Ethics and Natural Language Processing Surveys
Karën Fort | Alain Couillault

We present here the context and results of two surveys (a French one and an international one) concerning Ethics and NLP, which we designed and conducted between June and September 2015. These surveys follow other actions related to raising concern for ethics in our community, including a Journée d’études, a workshop and the Ethics and Big Data Charter. The concern for ethics shows to be quite similar in both surveys, despite a few differences which we present and discuss. The surveys also lead to think there is a growing awareness in the field concerning ethical issues, which translates into a willingness to get involved in ethics-related actions, to debate about the topic and to see ethics be included in major conferences themes. We finally discuss the limits of the surveys and the means of action we consider for the future. The raw data from the two surveys are freely available online.

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Open Data Vocabularies for Assigning Usage Rights to Data Resources from Translation Projects
David Lewis | Kaniz Fatema | Alfredo Maldonado | Brian Walshe | Arturo Calvo

An assessment of the intellectual property requirements for data used in machine-aided translation is provided based on a recent EC-funded legal review. This is compared against the capabilities offered by current linked open data standards from the W3C for publishing and sharing translation memories from translation projects, and proposals for adequately addressing the intellectual property needs of stakeholders in translation projects using open data vocabularies are suggested.

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Language Resource Citation: the ISLRN Dissemination and Further Developments
Valérie Mapelli | Vladimir Popescu | Lin Liu | Khalid Choukri

This article presents the latest dissemination activities and technical developments that were carried out for the International Standard Language Resource Number (ISLRN) service. It also recalls the main principle and submission process for providers to obtain their 13-digit ISLRN identifier. Up to March 2016, 2100 Language Resources were allocated an ISLRN number, not only ELRA’s and LDC’s catalogued Language Resources, but also the ones from other important organisations like the Joint Research Centre (JRC) and the Resource Management Agency (RMA) who expressed their strong support to this initiative. In the research field, not only assigning a unique identification number is important, but also referring to a Language Resource as an object per se (like publications) has now become an obvious requirement. The ISLRN could also become an important parameter to be considered to compute a Language Resource Impact Factor (LRIF) in order to recognize the merits of the producers of Language Resources. Integrating the ISLRN number into a LR-oriented bibliographical reference is thus part of the objective. The idea is to make use of a BibTeX entry that would take into account Language Resources items, including ISLRN.The ISLRN being a requested field within the LREC 2016 submission, we expect that several other LRs will be allocated an ISLRN number by the conference date. With this expansion, this number aims to be a spreadly-used LR citation instrument within works referring to LRs.

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Trends in HLT Research: A Survey of LDC’s Data Scholarship Program
Denise DiPersio | Christopher Cieri

Since its inception in 2010, the Linguistic Data Consortium’s data scholarship program has awarded no cost grants in data to 64 recipients from 26 countries. A survey of the twelve cycles to date ― two awards each in the Fall and Spring semesters from Fall 2010 through Spring 2016 ― yields an interesting view into graduate program research trends in human language technology and related fields and the particular data sets deemed important to support that research. The survey also reveals regions in which such activity appears to be on a rise, including in Arabic-speaking regions and portions of the Americas and Asia.

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Tweeting and Being Ironic in the Debate about a Political Reform: the French Annotated Corpus TWitter-MariagePourTous
Cristina Bosco | Mirko Lai | Viviana Patti | Daniela Virone

The paper introduces a new annotated French data set for Sentiment Analysis, which is a currently missing resource. It focuses on the collection from Twitter of data related to the socio-political debate about the reform of the bill for wedding in France. The design of the annotation scheme is described, which extends a polarity label set by making available tags for marking target semantic areas and figurative language devices. The annotation process is presented and the disagreement discussed, in particular, in the perspective of figurative language use and in that of the semantic oriented annotation, which are open challenges for NLP systems.

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Towards a Corpus of Violence Acts in Arabic Social Media
Ayman Alhelbawy | Poesio Massimo | Udo Kruschwitz

In this paper we present a new corpus of Arabic tweets that mention some form of violent event, developed to support the automatic identification of Human Rights Abuse. The dataset was manually labelled for seven classes of violence using crowdsourcing.

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TwiSty: A Multilingual Twitter Stylometry Corpus for Gender and Personality Profiling
Ben Verhoeven | Walter Daelemans | Barbara Plank

Personality profiling is the task of detecting personality traits of authors based on writing style. Several personality typologies exist, however, the Briggs-Myer Type Indicator (MBTI) is particularly popular in the non-scientific community, and many people use it to analyse their own personality and talk about the results online. Therefore, large amounts of self-assessed data on MBTI are readily available on social-media platforms such as Twitter. We present a novel corpus of tweets annotated with the MBTI personality type and gender of their author for six Western European languages (Dutch, German, French, Italian, Portuguese and Spanish). We outline the corpus creation and annotation, show statistics of the obtained data distributions and present first baselines on Myers-Briggs personality profiling and gender prediction for all six languages.

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Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Muhammad Imran | Prasenjit Mitra | Carlos Castillo

Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons. During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed timely and effectively. Processing social media information pose multiple challenges such as parsing noisy, brief and informal messages, learning information categories from the incoming stream of messages and classifying them into different classes among others. One of the basic necessities of many of these tasks is the availability of data, in particular human-annotated data. In this paper, we present human-annotated Twitter corpora collected during 19 different crises that took place between 2013 and 2015. To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. To deal with tweets language issues, we present human-annotated normalized lexical resources for different lexical variations.

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Functions of Code-Switching in Tweets: An Annotation Framework and Some Initial Experiments
Rafiya Begum | Kalika Bali | Monojit Choudhury | Koustav Rudra | Niloy Ganguly

Code-Switching (CS) between two languages is extremely common in communities with societal multilingualism where speakers switch between two or more languages when interacting with each other. CS has been extensively studied in spoken language by linguists for several decades but with the popularity of social-media and less formal Computer Mediated Communication, we now see a big rise in the use of CS in the text form. This poses interesting challenges and a need for computational processing of such code-switched data. As with any Computational Linguistic analysis and Natural Language Processing tools and applications, we need annotated data for understanding, processing, and generation of code-switched language. In this study, we focus on CS between English and Hindi Tweets extracted from the Twitter stream of Hindi-English bilinguals. We present an annotation scheme for annotating the pragmatic functions of CS in Hindi-English (Hi-En) code-switched tweets based on a linguistic analysis and some initial experiments.

We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper. Since the Japanese syntactic structure is usually annotated on the basis of unique chunk-based dependencies, we first introduce word-based dependencies by using a word unit called the Short Unit Word, which usually corresponds to an entry in the lexicon UniDic. Porting is done by mapping the part-of-speech tagset in UniDic to the universal part-of-speech tagset, and converting a constituent-based treebank to a typed dependency tree. The conversion is not straightforward, and we discuss the problems that arose in the conversion and the current solutions. A treebank consisting of 10,000 sentences was built by converting the existent resources and currently released to the public.

Cross-linguistically consistent annotation is necessary for sound comparative evaluation and cross-lingual learning experiments. It is also useful for multilingual system development and comparative linguistic studies. Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. In this paper, we describe v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages.

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Construction of an English Dependency Corpus incorporating Compound Function Words
Akihiko Kato | Hiroyuki Shindo | Yuji Matsumoto

The recognition of multiword expressions (MWEs) in a sentence is important for such linguistic analyses as syntactic and semantic parsing, because it is known that combining an MWE into a single token improves accuracy for various NLP tasks, such as dependency parsing and constituency parsing. However, MWEs are not annotated in Penn Treebank. Furthermore, when converting word-based dependency to MWE-aware dependency directly, one could combine nodes in an MWE into a single node. Nevertheless, this method often leads to the following problem: A node derived from an MWE could have multiple heads and the whole dependency structure including MWE might be cyclic. Therefore we converted a phrase structure to a dependency structure after establishing an MWE as a single subtree. This approach can avoid an occurrence of multiple heads and/or cycles. In this way, we constructed an English dependency corpus taking into account compound function words, which are one type of MWEs that serve as functional expressions. In addition, we report experimental results of dependency parsing using a constructed corpus.

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Adapting the TANL tool suite to Universal Dependencies
Maria Simi | Giuseppe Attardi

TANL is a suite of tools for text analytics based on the software architecture paradigm of data driven pipelines. The strategies for upgrading TANL to the use of Universal Dependencies range from a minimalistic approach consisting of introducing pre/post-processing steps into the native pipeline to revising the whole pipeline. We explore the issue in the context of the Italian Treebank, considering both the efforts involved, how to avoid losing linguistically relevant information and the loss of accuracy in the process. In particular we compare different strategies for parsing and discuss the implications of simplifying the pipeline when detailed part-of-speech and morphological annotations are not available, as it is the case for less resourceful languages. The experiments are relative to the Italian linguistic pipeline, but the use of different parsers in our evaluations and the avoidance of language specific tagging make the results general enough to be useful in helping the transition to UD for other languages.

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A Dependency Treebank of the Chinese Buddhist Canon
Tak-sum Wong | John Lee

We present a dependency treebank of the Chinese Buddhist Canon, which contains 1,514 texts with about 50 million Chinese characters. The treebank was created by an automatic parser trained on a smaller treebank, containing four manually annotated sutras (Lee and Kong, 2014). We report results on word segmentation, part-of-speech tagging and dependency parsing, and discuss challenges posed by the processing of medieval Chinese. In a case study, we exploit the treebank to examine verbs frequently associated with Buddha, and to analyze usage patterns of quotative verbs in direct speech. Our results suggest that certain quotative verbs imply status differences between the speaker and the listener.

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Automatic Biomedical Term Polysemy Detection
Juan Antonio Lossio-Ventura | Clement Jonquet | Mathieu Roche | Maguelonne Teisseire

Polysemy is the capacity for a word to have multiple meanings. Polysemy detection is a first step for Word Sense Induction (WSI), which allows to find different meanings for a term. The polysemy detection is also important for information extraction (IE) systems. In addition, the polysemy detection is important for building/enriching terminologies and ontologies. In this paper, we present a novel approach to detect if a biomedical term is polysemic, with the long term goal of enriching biomedical ontologies. This approach is based on the extraction of new features. In this context we propose to extract features following two manners: (i) extracted directly from the text dataset, and (ii) from an induced graph. Our method obtains an Accuracy and F-Measure of 0.978.

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Cro36WSD: A Lexical Sample for Croatian Word Sense Disambiguation
Domagoj Alagić | Jan Šnajder

We introduce Cro36WSD, a freely-available medium-sized lexical sample for Croatian word sense disambiguation (WSD).Cro36WSD comprises 36 words: 12 adjectives, 12 nouns, and 12 verbs, balanced across both frequency bands and polysemy levels. We adopt the multi-label annotation scheme in the hope of lessening the drawbacks of discrete sense inventories and obtaining more realistic annotations from human experts. Sense-annotated data is collected through multiple annotation rounds to ensure high-quality annotations: with a 115 person-hours effort we reached an inter-annotator agreement score of 0.877. We analyze the obtained data and perform a correlation analysis between several relevant variables, including word frequency, number of senses, sense distribution skewness, average annotation time, and the observed inter-annotator agreement (IAA). Using the obtained data, we compile multi- and single-labeled dataset variants using different label aggregation schemes. Finally, we evaluate three different baseline WSD models on both dataset variants and report on the insights gained. We make both dataset variants freely available.

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Addressing the MFS Bias in WSD systems
Marten Postma | Ruben Izquierdo | Eneko Agirre | German Rigau | Piek Vossen

Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses. We addressed the MFS bias in WSD systems by combining the output from a WSD system with a set of mostly static features to create a MFS classifier to decide when to and not to choose the MFS. The output from this MFS classifier, which is based on the Random Forest algorithm, is then used to modify the output from the original WSD system. We applied our classifier to one of the state-of-the-art supervised WSD systems, i.e. IMS, and to of the best state-of-the-art unsupervised WSD systems, i.e. UKB. Our main finding is that we are able to improve the system output in terms of choosing between the MFS and the less frequent senses. When we apply the MFS classifier to fine-grained WSD, we observe an improvement on the less frequent sense cases, whereas we maintain the overall recall.

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A Large-Scale Multilingual Disambiguation of Glosses
José Camacho-Collados | Claudio Delli Bovi | Alessandro Raganato | Roberto Navigli

Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline.

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Unsupervised Ranked Cross-Lingual Lexical Substitution for Low-Resource Languages
Stefan Ecker | Andrea Horbach | Stefan Thater

We propose an unsupervised system for a variant of cross-lingual lexical substitution (CLLS) to be used in a reading scenario in computer-assisted language learning (CALL), in which single-word translations provided by a dictionary are ranked according to their appropriateness in context. In contrast to most alternative systems, ours does not rely on either parallel corpora or machine translation systems, making it suitable for low-resource languages as the language to be learned. This is achieved by a graph-based scoring mechanism which can deal with ambiguous translations of context words provided by a dictionary. Due to this decoupling from the source language, we need monolingual corpus resources only for the target language, i.e. the language of the translation candidates. We evaluate our approach for the language pair Norwegian Nynorsk-English on an exploratory manually annotated gold standard and report promising results. When running our system on the original SemEval CLLS task, we rank 6th out of 18 (including 2 baselines and our 2 system variants) in the best evaluation.

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Information structure in the Potsdam Commentary Corpus: Topics
Manfred Stede | Sara Mamprin

The Potsdam Commentary Corpus is a collection of 175 German newspaper commentaries annotated on a variety of different layers. This paper introduces a new layer that covers the linguistic notion of information-structural topic (not to be confused with ‘topic’ as applied to documents in information retrieval). To our knowledge, this is the first larger topic-annotated resource for German (and one of the first for any language). We describe the annotation guidelines and the annotation process, and the results of an inter-annotator agreement study, which compare favourably to the related work. The annotated corpus is freely available for research.

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A Corpus of Clinical Practice Guidelines Annotated with the Importance of Recommendations
Jonathon Read | Erik Velldal | Marc Cavazza | Gersende Georg

In this paper we present the Corpus of REcommendation STrength (CREST), a collection of HTML-formatted clinical guidelines annotated with the location of recommendations. Recommendations are labelled with an author-provided indicator of their strength of importance. As data was drawn from many disparate authors, we define a unified scheme of importance labels, and provide a mapping for each guideline. We demonstrate the utility of the corpus and its annotations in some initial measurements investigating the type of language constructions associated with strong and weak recommendations, and experiments into promising features for recommendation classification, both with respect to strong and weak labels, and to all labels of the unified scheme. An error analysis indicates that, while there is a strong relationship between lexical choices and strength labels, there can be substantial variance in the choices made by different authors.

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The Methodius Corpus of Rhetorical Discourse Structures and Generated Texts
Amy Isard

Using the Methodius Natural Language Generation (NLG) System, we have created a corpus which consists of a collection of generated texts which describe ancient Greek artefacts. Each text is linked to two representations created as part of the NLG process. The first is a content plan, which uses rhetorical relations to describe the high-level discourse structure of the text, and the second is a logical form describing the syntactic structure, which is sent to the OpenCCG surface realization module to produce the final text output. In recent work, White and Howcroft (2015) have used the SPaRKy restaurant corpus, which contains similar combination of texts and representations, for their research on the induction of rules for the combination of clauses. In the first instance this corpus will be used to test their algorithms on an additional domain, and extend their work to include the learning of referring expression generation rules. As far as we know, the SPaRKy restaurant corpus is the only existing corpus of this type, and we hope that the creation of this new corpus in a different domain will provide a useful resource to the Natural Language Generation community.

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Applying Core Scientific Concepts to Context-Based Citation Recommendation
Daniel Duma | Maria Liakata | Amanda Clare | James Ravenscroft | Ewan Klein

The task of recommending relevant scientific literature for a draft academic paper has recently received significant interest. In our effort to ease the discovery of scientific literature and augment scientific writing, we aim to improve the relevance of results based on a shallow semantic analysis of the source document and the potential documents to recommend. We investigate the utility of automatic argumentative and rhetorical annotation of documents for this purpose. Specifically, we integrate automatic Core Scientific Concepts (CoreSC) classification into a prototype context-based citation recommendation system and investigate its usefulness to the task. We frame citation recommendation as an information retrieval task and we use the categories of the annotation schemes to apply different weights to the similarity formula. Our results show interesting and consistent correlations between the type of citation and the type of sentence containing the relevant information.

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SciCorp: A Corpus of English Scientific Articles Annotated for Information Status Analysis
Ina Roesiger

This paper presents SciCorp, a corpus of full-text English scientific papers of two disciplines, genetics and computational linguistics. The corpus comprises co-reference and bridging information as well as information status labels. Since SciCorp is annotated with both labels and the respective co-referent and bridging links, we believe it is a valuable resource for NLP researchers working on scientific articles or on applications such as co-reference resolution, bridging resolution or information status classification. The corpus has been reliably annotated by independent human coders with moderate inter-annotator agreement (average kappa = 0.71). In total, we have annotated 14 full papers containing 61,045 tokens and marked 8,708 definite noun phrases. The paper describes in detail the annotation scheme as well as the resulting corpus. The corpus is available for download in two different formats: in an offset-based format and for the co-reference annotations in the widely-used, tabular CoNLL-2012 format.

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Using lexical and Dependency Features to Disambiguate Discourse Connectives in Hindi
Rohit Jain | Himanshu Sharma | Dipti Sharma

Discourse parsing is a challenging task in NLP and plays a crucial role in discourse analysis. To enable discourse analysis for Hindi, Hindi Discourse Relations Bank was created on a subset of Hindi TreeBank. The benefits of a discourse analyzer in automated discourse analysis, question summarization and question answering domains has motivated us to begin work on a discourse analyzer for Hindi. In this paper, we focus on discourse connective identification for Hindi. We explore various available syntactic features for this task. We also explore the use of dependency tree parses present in the Hindi TreeBank and study the impact of the same on the performance of the system. We report that the novel dependency features introduced have a higher impact on precision, in comparison to the syntactic features previously used for this task. In addition, we report a high accuracy of 96% for this task.

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Annotating Topic Development in Information Seeking Queries
Marta Andersson | Adnan Öztürel | Silvia Pareti

This paper contributes to the limited body of empirical research in the domain of discourse structure of information seeking queries. We describe the development of an annotation schema for coding topic development in information seeking queries and the initial observations from a pilot sample of query sessions. The main idea that we explore is the relationship between constant and variable discourse entities and their role in tracking changes in the topic progression. We argue that the topicalized entities remain stable across development of the discourse and can be identified by a simple mechanism where anaphora resolution is a precursor. We also claim that a corpus annotated in this framework can be used as training data for dialogue management and computational semantics systems.

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Searching in the Penn Discourse Treebank Using the PML-Tree Query
Jiří Mírovský | Lucie Poláková | Jan Štěpánek

The PML-Tree Query is a general, powerful and user-friendly system for querying richly linguistically annotated treebanks. The paper shows how the PML-Tree Query can be used for searching for discourse relations in the Penn Discourse Treebank 2.0 mapped onto the syntactic annotation of the Penn Treebank.

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The OpenCourseWare Metadiscourse (OCWMD) Corpus
Ghada Alharbi | Thomas Hain

This study describes a new corpus of over 60,000 hand-annotated metadiscourse acts from 106 OpenCourseWare lectures, from two different disciplines: Physics and Economics. Metadiscourse is a set of linguistic expressions that signal different functions in the discourse. This type of language is hypothesised to be helpful in finding a structure in unstructured text, such as lectures discourse. A brief summary is provided about the annotation scheme and labelling procedures, inter-annotator reliability statistics, overall distributional statistics, a description of auxiliary data that will be distributed with the corpus, and information relating to how to obtain the data. The results provide a deeper understanding of lecture structure and confirm the reliable coding of metadiscursive acts in academic lectures across different disciplines. The next stage of our research will be to build a classification model to automate the tagging process, instead of manual annotation, which take time and efforts. This is in addition to the use of these tags as indicators of the higher level structure of lecture discourse.

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Ubuntu-fr: A Large and Open Corpus for Multi-modal Analysis of Online Written Conversations
Nicolas Hernandez | Soufian Salim | Elizaveta Loginova Clouet

We present a large, free, French corpus of online written conversations extracted from the Ubuntu platform’s forums, mailing lists and IRC channels. The corpus is meant to support multi-modality and diachronic studies of online written conversations. We choose to build the corpus around a robust metadata model based upon strong principles, such as the “stand off” annotation principle. We detail the model, we explain how the data was collected and processed - in terms of meta-data, text and conversation - and we detail the corpus’contents through a series of meaningful statistics. A portion of the corpus - about 4,700 sentences from emails, forum posts and chat messages sent in November 2014 - is annotated in terms of dialogue acts and sentiment. We discuss how we adapted our dialogue act taxonomy from the DIT++ annotation scheme and how the data was annotated, before presenting our results as well as a brief qualitative analysis of the annotated data.

We present the DUEL corpus, consisting of 24 hours of natural, face-to-face, loosely task-directed dialogue in German, French and Mandarin Chinese. The corpus is uniquely positioned as a cross-linguistic, multimodal dialogue resource controlled for domain. DUEL includes audio, video and body tracking data and is transcribed and annotated for disfluency, laughter and exclamations.

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Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
Guntis Barzdins | Steve Renals | Didzis Gosko

The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address these two problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.

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Exploring the Realization of Irony in Twitter Data
Cynthia Van Hee | Els Lefever | Véronique Hoste

Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.

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Discriminating Similar Languages: Evaluations and Explorations
Cyril Goutte | Serge Léger | Shervin Malmasi | Marcos Zampieri

We present an analysis of the performance of machine learning classifiers on discriminating between similar languages and language varieties. We carried out a number of experiments using the results of the two editions of the Discriminating between Similar Languages (DSL) shared task. We investigate the progress made between the two tasks, estimate an upper bound on possible performance using ensemble and oracle combination, and provide learning curves to help us understand which languages are more challenging. A number of difficult sentences are identified and investigated further with human annotation

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Compilation of an Arabic Children’s Corpus
Latifa Al-Sulaiti | Noorhan Abbas | Claire Brierley | Eric Atwell | Ayman Alghamdi

Inspired by the Oxford Children’s Corpus, we have developed a prototype corpus of Arabic texts written and/or selected for children. Our Arabic Children’s Corpus of 2950 documents and nearly 2 million words has been collected manually from the web during a 3-month project. It is of high quality, and contains a range of different children’s genres based on sources located, including classic tales from The Arabian Nights, and popular fictional characters such as Goha. We anticipate that the current and subsequent versions of our corpus will lead to interesting studies in text classification, language use, and ideology in children’s texts.

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Quality Assessment of the Reuters Vol. 2 Multilingual Corpus
Robin Eriksson

We introduce a framework for quality assurance of corpora, and apply it to the Reuters Multilingual Corpus (RCV2). The results of this quality assessment of this standard newsprint corpus reveal a significant duplication problem and, to a lesser extent, a problem with corrupted articles. From the raw collection of some 487,000 articles, almost one tenth are trivial duplicates. A smaller fraction of articles appear to be corrupted and should be excluded for that reason. The detailed results are being made available as on-line appendices to this article. This effort also demonstrates the beginnings of a constraint-based methodological framework for quality assessment and quality assurance for corpora. As a first implementation of this framework, we have investigated constraints to verify sample integrity, and to diagnose sample duplication, entropy aberrations, and tagging inconsistencies. To help identify near-duplicates in the corpus, we have employed both entropy measurements and a simple byte bigram incidence digest.

Attribution bias refers to the tendency of people to attribute successes to their own abilities but failures to external factors. In a business context an internal factor might be the restructuring of the firm and an external factor might be an unfavourable change in exchange or interest rates. In accounting research, the presence of an attribution bias has been demonstrated for the narrative sections of the annual financial reports. Previous studies have applied manual content analysis to this problem but in this paper we present novel work to automate the analysis of attribution bias through using machine learning algorithms. Previous studies have only applied manual content analysis on a small scale to reveal such a bias in the narrative section of annual financial reports. In our work a group of experts in accounting and finance labelled and annotated a list of 32,449 sentences from a random sample of UK Preliminary Earning Announcements (PEAs) to allow us to examine whether sentences in PEAs contain internal or external attribution and which kinds of attributions are linked to positive or negative performance. We wished to examine whether human annotators could agree on coding this difficult task and whether Machine Learning (ML) could be applied reliably to replicate the coding process on a much larger scale. Our best machine learning algorithm correctly classified performance sentences with 70% accuracy and detected tone and attribution in financial PEAs with accuracy of 79%.

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A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances
Roman Sergienko | Muhammad Shan | Wolfgang Minker

The paper describes a comparative study of existing and novel text preprocessing and classification techniques for domain detection of user utterances. Two corpora are considered. The first one contains customer calls to a call centre for further call routing; the second one contains answers of call centre employees with different kinds of customer orientation behaviour. Seven different unsupervised and supervised term weighting methods were applied. The collective use of term weighting methods is proposed for classification effectiveness improvement. Four different dimensionality reduction methods were applied: stop-words filtering with stemming, feature selection based on term weights, feature transformation based on term clustering, and a novel feature transformation method based on terms belonging to classes. As classification algorithms we used k-NN and a SVM-based algorithm. The numerical experiments have shown that the simultaneous use of the novel proposed approaches (collectives of term weighting methods and the novel feature transformation method) allows reaching the high classification results with very small number of features.

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UPPC - Urdu Paraphrase Plagiarism Corpus
Muhammad Sharjeel | Paul Rayson | Rao Muhammad Adeel Nawab

Paraphrase plagiarism is a significant and widespread problem and research shows that it is hard to detect. Several methods and automatic systems have been proposed to deal with it. However, evaluation and comparison of such solutions is not possible because of the unavailability of benchmark corpora with manual examples of paraphrase plagiarism. To deal with this issue, we present the novel development of a paraphrase plagiarism corpus containing simulated (manually created) examples in the Urdu language - a language widely spoken around the world. This resource is the first of its kind developed for the Urdu language and we believe that it will be a valuable contribution to the evaluation of paraphrase plagiarism detection systems.

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Identifying Content Types of Messages Related to Open Source Software Projects
Yannis Korkontzelos | Paul Thompson | Sophia Ananiadou

Assessing the suitability of an Open Source Software project for adoption requires not only an analysis of aspects related to the code, such as code quality, frequency of updates and new version releases, but also an evaluation of the quality of support offered in related online forums and issue trackers. Understanding the content types of forum messages and issue trackers can provide information about the extent to which requests are being addressed and issues are being resolved, the percentage of issues that are not being fixed, the cases where the user acknowledged that the issue was successfully resolved, etc. These indicators can provide potential adopters of the OSS with estimates about the level of available support. We present a detailed hierarchy of content types of online forum messages and issue tracker comments and a corpus of messages annotated accordingly. We discuss our experiments to classify forum messages and issue tracker comments into content-related classes, i.e.~to assign them to nodes of the hierarchy. The results are very encouraging.

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Emotion Corpus Construction Based on Selection from Hashtags
Minglei Li | Yunfei Long | Lu Qin | Wenjie Li

The availability of labelled corpus is of great importance for supervised learning in emotion classification tasks. Because it is time-consuming to manually label text, hashtags have been used as naturally annotated labels to obtain a large amount of labelled training data from microblog. However, natural hashtags contain too much noise for it to be used directly in learning algorithms. In this paper, we design a three-stage semi-automatic method to construct an emotion corpus from microblogs. Firstly, a lexicon based voting approach is used to verify the hashtag automatically. Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels. Finally, the remaining data will be manually examined to filter out the noisy data. Out of about 48K filtered Chinese microblogs, 39k microblogs are selected to form the final corpus with the Kappa value reaching over 0.92 for the automatic parts and over 0.81 for the manual part. The proportion of automatic selection reaches 54.1%. Thus, the method can reduce about 44.5% of manual workload for acquiring quality data. Experiment on a classifier trained on this corpus shows that it achieves comparable results compared to the manually annotated NLP&CC2013 corpus.

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Comparing the Level of Code-Switching in Corpora
Björn Gambäck | Amitava Das

Social media texts are often fairly informal and conversational, and when produced by bilinguals tend to be written in several different languages simultaneously, in the same way as conversational speech. The recent availability of large social media corpora has thus also made large-scale code-switched resources available for research. The paper addresses the issues of evaluation and comparison these new corpora entail, by defining an objective measure of corpus level complexity of code-switched texts. It is also shown how this formal measure can be used in practice, by applying it to several code-switched corpora.

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Evaluation of the KIT Lecture Translation System
Markus Müller | Sarah Fünfer | Sebastian Stüker | Alex Waibel

To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next.

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The ACL RD-TEC 2.0: A Language Resource for Evaluating Term Extraction and Entity Recognition Methods
Behrang QasemiZadeh | Anne-Kathrin Schumann

This paper introduces the ACL Reference Dataset for Terminology Extraction and Classification, version 2.0 (ACL RD-TEC 2.0). The ACL RD-TEC 2.0 has been developed with the aim of providing a benchmark for the evaluation of term and entity recognition tasks based on specialised text from the computational linguistics domain. This release of the corpus consists of 300 abstracts from articles in the ACL Anthology Reference Corpus, published between 1978–2006. In these abstracts, terms (i.e., single or multi-word lexical units with a specialised meaning) are manually annotated. In addition to their boundaries in running text, annotated terms are classified into one of the seven categories method, tool, language resource (LR), LR product, model, measures and measurements, and other. To assess the quality of the annotations and to determine the difficulty of this annotation task, more than 171 of the abstracts are annotated twice, independently, by each of the two annotators. In total, 6,818 terms are identified and annotated in more than 1300 sentences, resulting in a specialised vocabulary made of 3,318 lexical forms, mapped to 3,471 concepts. We explain the development of the annotation guidelines and discuss some of the challenges we encountered in this annotation task.

We present our guidelines and annotation procedure to create a human corrected machine translated post-edited corpus for the Modern Standard Arabic. Our overarching goal is to use the annotated corpus to develop automatic machine translation post-editing systems for Arabic that can be used to help accelerate the human revision process of translated texts. The creation of any manually annotated corpus usually presents many challenges. In order to address these challenges, we created comprehensive and simplified annotation guidelines which were used by a team of five annotators and one lead annotator. In order to ensure a high annotation agreement between the annotators, multiple training sessions were held and regular inter-annotator agreement measures were performed to check the annotation quality. The created corpus of manual post-edited translations of English to Arabic articles is the largest to date for this language pair.

This work addresses the need to aid Machine Translation (MT) development cycles with a complete workflow of MT evaluation methods. Our aim is to assess, compare and improve MT system variants. We hereby report on novel tools and practices that support various measures, developed in order to support a principled and informed approach of MT development. Our toolkit for automatic evaluation showcases quick and detailed comparison of MT system variants through automatic metrics and n-gram feedback, along with manual evaluation via edit-distance, error annotation and task-based feedback.

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Generating Task-Pertinent sorted Error Lists for Speech Recognition
Olivier Galibert | Mohamed Ameur Ben Jannet | Juliette Kahn | Sophie Rosset

Automatic Speech recognition (ASR) is one of the most widely used components in spoken language processing applications. ASR errors are of varying importance with respect to the application, making error analysis keys to improving speech processing applications. Knowing the most serious errors for the applicative case is critical to build better systems. In the context of Automatic Speech Recognition (ASR) used as a first step towards Named Entity Recognition (NER) in speech, error seriousness is usually determined by their frequency, due to the use of the WER as metric to evaluate the ASR output, despite the emergence of more relevant measures in the literature. We propose to use a different evaluation metric form the literature in order to classify ASR errors according to their seriousness for NER. Our results show that the ASR errors importance is ranked differently depending on the used evaluation metric. A more detailed analysis shows that the estimation of the error impact given by the ATENE metric is more adapted to the NER task than the estimation based only on the most used frequency metric WER.

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A Study of Reuse and Plagiarism in LREC papers
Gil Francopoulo | Joseph Mariani | Patrick Paroubek

The aim of this experiment is to present an easy way to compare fragments of texts in order to detect (supposed) results of copy & paste operations between articles in the domain of Natural Language Processing (NLP). The search space of the comparisons is a corpus labeled as NLP4NLP gathering a large part of the NLP field. The study is centered on LREC papers in both directions, first with an LREC paper borrowing a fragment of text from the collection, and secondly in the reverse direction with fragments of LREC documents borrowed and inserted in the collection.

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Developing a Dataset for Evaluating Approaches for Document Expansion with Images
Debasis Ganguly | Iacer Calixto | Gareth Jones

Motivated by the adage that a “picture is worth a thousand words” it can be reasoned that automatically enriching the textual content of a document with relevant images can increase the readability of a document. Moreover, features extracted from the additional image data inserted into the textual content of a document may, in principle, be also be used by a retrieval engine to better match the topic of a document with that of a given query. In this paper, we describe our approach of building a ground truth dataset to enable further research into automatic addition of relevant images to text documents. The dataset is comprised of the official ImageCLEF 2010 collection (a collection of images with textual metadata) to serve as the images available for automatic enrichment of text, a set of 25 benchmark documents that are to be enriched, which in this case are children’s short stories, and a set of manually judged relevant images for each query story obtained by the standard procedure of depth pooling. We use this benchmark dataset to evaluate the effectiveness of standard information retrieval methods as simple baselines for this task. The results indicate that using the whole story as a weighted query, where the weight of each query term is its tf-idf value, achieves an precision of 0:1714 within the top 5 retrieved images on an average.

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More than Word Cooccurrence: Exploring Support and Opposition in International Climate Negotiations with Semantic Parsing
Pablo Ruiz Fabo | Clément Plancq | Thierry Poibeau

Text analysis methods widely used in digital humanities often involve word co-occurrence, e.g. concept co-occurrence networks. These methods provide a useful corpus overview, but cannot determine the predicates that relate co-occurring concepts. Our goal was identifying propositions expressing the points supported or opposed by participants in international climate negotiations. Word co-occurrence methods were not sufficient, and an analysis based on open relation extraction had limited coverage for nominal predicates. We present a pipeline which identifies the points that different actors support and oppose, via a domain model with support/opposition predicates, and analysis rules that exploit the output of semantic role labelling, syntactic dependencies and anaphora resolution. Entity linking and keyphrase extraction are also performed on the propositions related to each actor. A user interface allows examining the main concepts in points supported or opposed by each participant, which participants agree or disagree with each other, and about which issues. The system is an example of tools that digital humanities scholars are asking for, to render rich textual information (beyond word co-occurrence) more amenable to quantitative treatment. An evaluation of the tool was satisfactory.

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A Sequence Model Approach to Relation Extraction in Portuguese
Sandra Collovini | Gabriel Machado | Renata Vieira

The task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language processing techniques employed. This task aims at identifying and classifying semantic relations that occur between entities recognized in a given text. In this paper, we evaluated a Conditional Random Fields classifier for the extraction of any relation descriptor occurring between named entities (Organisation, Person and Place categories), as well as pre-defined relation types between these entities in Portuguese texts.

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Evaluation Set for Slovak News Information Retrieval
Daniel Hládek | Jan Staš | Jozef Juhár

This work proposes an information retrieval evaluation set for the Slovak language. A set of 80 queries written in the natural language is given together with the set of relevant documents. The document set contains 3980 newspaper articles sorted into 6 categories. Each document in the result set is manually annotated for relevancy with its corresponding query. The evaluation set is mostly compatible with the Cranfield test collection using the same methodology for queries and annotation of relevancy. In addition to that it provides annotation for document title, author, publication date and category that can be used for evaluation of automatic document clustering and categorization.

This paper proposes how to utilize a search engine in order to predict market shares. We propose to compare rates of concerns of those who search for Web pages among several companies which supply products, given a specific products domain. We measure concerns of those who search for Web pages through search engine suggests. Then, we analyze whether rates of concerns of those who search for Web pages have certain correlation with actual market share. We show that those statistics have certain correlations. We finally propose how to predict the market share of a specific product genre based on the rates of concerns of those who search for Web pages.

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TermITH-Eval: a French Standard-Based Resource for Keyphrase Extraction Evaluation
Adrien Bougouin | Sabine Barreaux | Laurent Romary | Florian Boudin | Béatrice Daille

Keyphrase extraction is the task of finding phrases that represent the important content of a document. The main aim of keyphrase extraction is to propose textual units that represent the most important topics developed in a document. The output keyphrases of automatic keyphrase extraction methods for test documents are typically evaluated by comparing them to manually assigned reference keyphrases. Each output keyphrase is considered correct if it matches one of the reference keyphrases. However, the choice of the appropriate textual unit (keyphrase) for a topic is sometimes subjective and evaluating by exact matching underestimates the performance. This paper presents a dataset of evaluation scores assigned to automatically extracted keyphrases by human evaluators. Along with the reference keyphrases, the manual evaluations can be used to validate new evaluation measures. Indeed, an evaluation measure that is highly correlated to the manual evaluation is appropriate for the evaluation of automatic keyphrase extraction methods.

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The Royal Society Corpus: From Uncharted Data to Corpus
Hannah Kermes | Stefania Degaetano-Ortlieb | Ashraf Khamis | Jörg Knappen | Elke Teich

We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665―1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.

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Building Evaluation Datasets for Consumer-Oriented Information Retrieval
Lorraine Goeuriot | Liadh Kelly | Guido Zuccon | Joao Palotti

Common people often experience difficulties in accessing relevant, correct, accurate and understandable health information online. Developing search techniques that aid these information needs is challenging. In this paper we present the datasets created by CLEF eHealth Lab from 2013-2015 for evaluation of search solutions to support common people finding health information online. Specifically, the CLEF eHealth information retrieval (IR) task of this Lab has provided the research community with benchmarks for evaluating consumer-centered health information retrieval, thus fostering research and development aimed to address this challenging problem. Given consumer queries, the goal of the task is to retrieve relevant documents from the provided collection of web pages. The shared datasets provide a large health web crawl, queries representing people’s real world information needs, and relevance assessment judgements for the queries.

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A Dataset for Open Event Extraction in English
Kiem-Hieu Nguyen | Xavier Tannier | Olivier Ferret | Romaric Besançon

This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws & Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data.

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Phoneme Alignment Using the Information on Phonological Processes in Continuous Speech
Daniil Kocharov

The current study focuses on optimization of Levenshtein algorithm for the purpose of computing the optimal alignment between two phoneme transcriptions of spoken utterance containing sequences of phonetic symbols. The alignment is computed with the help of a confusion matrix in which costs for phonetic symbol deletion, insertion and substitution are defined taking into account various phonological processes that occur in fluent speech, such as anticipatory assimilation, phone elision and epenthesis. The corpus containing about 30 hours of Russian read speech was used to evaluate the presented algorithms. The experimental results have shown significant reduction of misalignment rate in comparison with the baseline Levenshtein algorithm: the number of errors has been reduced from 1.1 % to 0.28 %

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CoRuSS - a New Prosodically Annotated Corpus of Russian Spontaneous Speech
Tatiana Kachkovskaia | Daniil Kocharov | Pavel Skrelin | Nina Volskaya

This paper describes speech data recording, processing and annotation of a new speech corpus CoRuSS (Corpus of Russian Spontaneous Speech), which is based on connected communicative speech recorded from 60 native Russian male and female speakers of different age groups (from 16 to 77). Some Russian speech corpora available at the moment contain plain orthographic texts and provide some kind of limited annotation, but there are no corpora providing detailed prosodic annotation of spontaneous conversational speech. This corpus contains 30 hours of high quality recorded spontaneous Russian speech, half of it has been transcribed and prosodically labeled. The recordings consist of dialogues between two speakers, monologues (speakers’ self-presentations) and reading of a short phonetically balanced text. Since the corpus is labeled for a wide range of linguistic - phonetic and prosodic - information, it provides basis for empirical studies of various spontaneous speech phenomena as well as for comparison with those we observe in prepared read speech. Since the corpus is designed as a open-access resource of speech data, it will also make possible to advance corpus-based analysis of spontaneous speech data across languages and speech technology development as well.

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Defining and Counting Phonological Classes in Cross-linguistic Segment Databases
Dan Dediu | Scott Moisik

Recently, there has been an explosion in the availability of large, good-quality cross-linguistic databases such as WALS (Dryer & Haspelmath, 2013), Glottolog (Hammarstrom et al., 2015) and Phoible (Moran & McCloy, 2014). Databases such as Phoible contain the actual segments used by various languages as they are given in the primary language descriptions. However, this segment-level representation cannot be used directly for analyses that require generalizations over classes of segments that share theoretically interesting features. Here we present a method and the associated R (R Core Team, 2014) code that allows the flexible definition of such meaningful classes and that can identify the sets of segments falling into such a class for any language inventory. The method and its results are important for those interested in exploring cross-linguistic patterns of phonetic and phonological diversity and their relationship to extra-linguistic factors and processes such as climate, economics, history or human genetics.

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Introducing the SEA_AP: an Enhanced Tool for Automatic Prosodic Analysis
Marta Martínez | Rocío Varela | Carmen García Mateo | Elisa Fernández Rei | Adela Martínez Calvo

SEA_AP (Segmentador e Etiquetador Automático para Análise Prosódica, Automatic Segmentation and Labelling for Prosodic Analysis) toolkit is an application that performs audio segmentation and labelling to create a TextGrid file which will be used to launch a prosodic analysis using Praat. In this paper, we want to describe the improved functionality of the tool achieved by adding a dialectometric analysis module using R scripts. The dialectometric analysis includes computing correlations among F0 curves and it obtains prosodic distances among the different variables of interest (location, speaker, structure, etc.). The dialectometric analysis requires large databases in order to be adequately computed, and automatic segmentation and labelling can create them thanks to a procedure less costly than the manual alternative. Thus, the integration of these tools into the SEA_AP allows to propose a distribution of geoprosodic areas by means of a quantitative method, which completes the traditional dialectological point of view. The current version of the SEA_AP toolkit is capable of analysing Galician, Spanish and Brazilian Portuguese data, and hence the distances between several prosodic linguistic varieties can be measured at present.

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A Machine Learning based Music Retrieval and Recommendation System
Naziba Mostafa | Yan Wan | Unnayan Amitabh | Pascale Fung

In this paper, we present a music retrieval and recommendation system using machine learning techniques. We propose a query by humming system for music retrieval that uses deep neural networks for note transcription and a note-based retrieval system for retrieving the correct song from the database. We evaluate our query by humming system using the standard MIREX QBSH dataset. We also propose a similar artist recommendation system which recommends similar artists based on acoustic features of the artists’ music, online text descriptions of the artists and social media data. We use supervised machine learning techniques over all our features and compare our recommendation results to those produced by a popular similar artist recommendation website.

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CirdoX: an on/off-line multisource speech and sound analysis software
Frédéric Aman | Michel Vacher | François Portet | William Duclot | Benjamin Lecouteux

Vocal User Interfaces in domestic environments recently gained interest in the speech processing community. This interest is due to the opportunity of using it in the framework of Ambient Assisted Living both for home automation (vocal command) and for call for help in case of distress situations, i.e. after a fall. C IRDO X, which is a modular software, is able to analyse online the audio environment in a home, to extract the uttered sentences and then to process them thanks to an ASR module. Moreover, this system perfoms non-speech audio event classification; in this case, specific models must be trained. The software is designed to be modular and to process on-line the audio multichannel stream. Some exemples of studies in which C IRDO X was involved are described. They were operated in real environment, namely a Living lab environment.

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Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Matthias Sperber | Graham Neubig | Satoshi Nakamura | Alex Waibel

Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance.

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A Framework for Collecting Realistic Recordings of Dysarthric Speech - the homeService Corpus
Mauro Nicolao | Heidi Christensen | Stuart Cunningham | Phil Green | Thomas Hain

This paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of speakers with severe dysarthria. The majority of the homeService corpus is recorded in real home environments where voice control is often the normal means by which users interact with their devices. The collection of the corpus is motivated by the shortage of realistic dysarthric speech corpora available to the scientific community. Along with the details on how the data is organised and how it can be accessed, a brief description of the framework used to make the recordings is provided. Finally, the performance of the homeService automatic recogniser for dysarthric speech trained with single-speaker data from the corpus is provided as an initial baseline. Access to the homeService corpus is provided through the dedicated web page at http://mini.dcs.shef.ac.uk/resources/homeservice-corpus/. This will also have the most updated description of the data. At the time of writing the collection process is still ongoing.

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Automatic Anomaly Detection for Dysarthria across Two Speech Styles: Read vs Spontaneous Speech
Imed Laaridh | Corinne Fredouille | Christine Meunier

Perceptive evaluation of speech disorders is still the standard method in clinical practice for the diagnosing and the following of the condition progression of patients. Such methods include different tasks such as read speech, spontaneous speech, isolated words, sustained vowels, etc. In this context, automatic speech processing tools have proven pertinence in speech quality evaluation and assistive technology-based applications. Though, a very few studies have investigated the use of automatic tools on spontaneous speech. This paper investigates the behavior of an automatic phone-based anomaly detection system when applied on read and spontaneous French dysarthric speech. The behavior of the automatic tool reveals interesting inter-pathology differences across speech styles.

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TTS for Low Resource Languages: A Bangla Synthesizer
Alexander Gutkin | Linne Ha | Martin Jansche | Knot Pipatsrisawat | Richard Sproat

We present a text-to-speech (TTS) system designed for the dialect of Bengali spoken in Bangladesh. This work is part of an ongoing effort to address the needs of under-resourced languages. We propose a process for streamlining the bootstrapping of TTS systems for under-resourced languages. First, we use crowdsourcing to collect the data from multiple ordinary speakers, each speaker recording small amount of sentences. Second, we leverage an existing text normalization system for a related language (Hindi) to bootstrap a linguistic front-end for Bangla. Third, we employ statistical techniques to construct multi-speaker acoustic models using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Hidden Markov Model (HMM) approaches. We then describe our experiments that show that the resulting TTS voices score well in terms of their perceived quality as measured by Mean Opinion Score (MOS) evaluations.

With the increasing amount of audiovisual and digital data deriving from televisual and radiophonic sources, professional archives such as INA, France’s national audiovisual institute, acknowledge a growing need for efficient indexing tools. In this paper, we describe the Speech Trax system that aims at analyzing the audio content of TV and radio documents. In particular, we focus on the speaker tracking task that is very valuable for indexing purposes. First, we detail the overall architecture of the system and show the results obtained on a large-scale experiment, the largest to our knowledge for this type of content (about 1,300 speakers). Then, we present the Speech Trax demonstrator that gathers the results of various automatic speech processing techniques on top of our speaker tracking system (speaker diarization, speech transcription, etc.). Finally, we provide insight on the obtained performances and suggest hints for future improvements.

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Web Chat Conversations from Contact Centers: a Descriptive Study
Géraldine Damnati | Aleksandra Guerraz | Delphine Charlet

In this article we propose a descriptive study of a chat conversations corpus from an assistance contact center. Conversations are described from several view points, including interaction analysis, language deviation analysis and typographic expressivity marks analysis. We provide in particular a detailed analysis of language deviations that are encountered in our corpus of 230 conversations, corresponding to 6879 messages and 76839 words. These deviations may be challenging for further syntactic and semantic parsing. Analysis is performed with a distinction between Customer messages and Agent messages. On the overall only 4% of the observed words are misspelled but 26% of the messages contain at least one erroneous word (rising to 40% when focused on Customer messages). Transcriptions of telephone conversations from an assistance call center are also studied, allowing comparisons between these two interaction modes to be drawn. The study reveals significant differences in terms of conversation flow, with an increased efficiency for chat conversations in spite of longer temporal span.

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Identification of Drug-Related Medical Conditions in Social Media
François Morlane-Hondère | Cyril Grouin | Pierre Zweigenbaum

Monitoring social media has been shown to be an interesting approach for the early detection of drug adverse effects. In this paper, we describe a system which extracts medical entities in French drug reviews written by users. We focus on the identification of medical conditions, which is based on the concept of post-coordination: we first extract minimal medical-related entities (pain, stomach) then we combine them to identify complex ones (It was the worst [pain I ever felt in my stomach]). These two steps are respectively performed by two classifiers, the first being based on Conditional Random Fields and the second one on Support Vector Machines. The overall results of the minimal entity classifier are the following: P=0.926; R=0.849; F1=0.886. A thourough analysis of the feature set shows that, when combined with word lemmas, clusters generated by word2vec are the most valuable features. When trained on the output of the first classifier, the second classifier’s performances are the following: p=0.683;r=0.956;f1=0.797. The addition of post-processing rules did not add any significant global improvement but was found to modify the precision/recall ratio.

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Creating a Lexicon of Bavarian Dialect by Means of Facebook Language Data and Crowdsourcing
Manuel Burghardt | Daniel Granvogl | Christian Wolff

Data acquisition in dialectology is typically a tedious task, as dialect samples of spoken language have to be collected via questionnaires or interviews. In this article, we suggest to use the “web as a corpus” approach for dialectology. We present a case study that demonstrates how authentic language data for the Bavarian dialect (ISO 639-3:bar) can be collected automatically from the social network Facebook. We also show that Facebook can be used effectively as a crowdsourcing platform, where users are willing to translate dialect words collaboratively in order to create a common lexicon of their Bavarian dialect. Key insights from the case study are summarized as “lessons learned”, together with suggestions for future enhancements of the lexicon creation approach.

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A Corpus of Wikipedia Discussions: Over the Years, with Topic, Power and Gender Labels
Vinodkumar Prabhakaran | Owen Rambow

In order to gain a deep understanding of how social context manifests in interactions, we need data that represents interactions from a large community of people over a long period of time, capturing different aspects of social context. In this paper, we present a large corpus of Wikipedia Talk page discussions that are collected from a broad range of topics, containing discussions that happened over a period of 15 years. The dataset contains 166,322 discussion threads, across 1236 articles/topics that span 15 different topic categories or domains. The dataset also captures whether the post is made by an registered user or not, and whether he/she was an administrator at the time of making the post. It also captures the Wikipedia age of editors in terms of number of months spent as an editor, as well as their gender. This corpus will be a valuable resource to investigate a variety of computational sociolinguistics research questions regarding online social interactions.

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Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Jon Chamberlain | Massimo Poesio | Udo Kruschwitz

Natural Language Engineering tasks require large and complex annotated datasets to build more advanced models of language. Corpora are typically annotated by several experts to create a gold standard; however, there are now compelling reasons to use a non-expert crowd to annotate text, driven by cost, speed and scalability. Phrase Detectives Corpus 1.0 is an anaphorically-annotated corpus of encyclopedic and narrative text that contains a gold standard created by multiple experts, as well as a set of annotations created by a large non-expert crowd. Analysis shows very good inter-expert agreement (kappa=.88-.93) but a more variable baseline crowd agreement (kappa=.52-.96). Encyclopedic texts show less agreement (and by implication are harder to annotate) than narrative texts. The release of this corpus is intended to encourage research into the use of crowds for text annotation and the development of more advanced, probabilistic language models, in particular for anaphoric coreference.

This paper presents Summ-it++, an enriched version the Summ-it corpus. In this new version, the corpus has received new semantic layers, named entity categories and relations between named entities, adding to the previous coreference annotation. In addition, we change the original Summ-it format to SemEval

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Towards Multiple Antecedent Coreference Resolution in Specialized Discourse
Alicia Burga | Sergio Cajal | Joan Codina-Filbà | Leo Wanner

Despite the popularity of coreference resolution as a research topic, the overwhelming majority of the work in this area focused so far on single antecedence coreference only. Multiple antecedent coreference (MAC) has been largely neglected. This can be explained by the scarcity of the phenomenon of MAC in generic discourse. However, in specialized discourse such as patents, MAC is very dominant. It seems thus unavoidable to address the problem of MAC resolution in the context of tasks related to automatic patent material processing, among them abstractive summarization, deep parsing of patents, construction of concept maps of the inventions, etc. We present the first version of an operational rule-based MAC resolution strategy for patent material that covers the three major types of MAC: (i) nominal MAC, (ii) MAC with personal / relative pronouns, and MAC with reflexive / reciprocal pronouns. The evaluation shows that our strategy performs well in terms of precision and recall.

This paper presents a second release of the ARRAU dataset: a multi-domain corpus with thorough linguistically motivated annotation of anaphora and related phenomena. Building upon the first release almost a decade ago, a considerable effort had been invested in improving the data both quantitatively and qualitatively. Thus, we have doubled the corpus size, expanded the selection of covered phenomena to include referentiality and genericity and designed and implemented a methodology for enforcing the consistency of the manual annotation. We believe that the new release of ARRAU provides a valuable material for ongoing research in complex cases of coreference as well as for a variety of related tasks. The corpus is publicly available through LDC.

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An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text
Eric Yeh | John Niekrasz | Dayne Freitag | Richard Rohwer

We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of “naturally” occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.

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Learning Thesaurus Relations from Distributional Features
Rosa Tsegaye Aga | Christian Wartena | Lucas Drumond | Lars Schmidt-Thieme

In distributional semantics words are represented by aggregated context features. The similarity of words can be computed by comparing their feature vectors. Thus, we can predict whether two words are synonymous or similar with respect to some other semantic relation. We will show on six different datasets of pairs of similar and non-similar words that a supervised learning algorithm on feature vectors representing pairs of words outperforms cosine similarity between vectors representing single words. We compared different methods to construct a feature vector representing a pair of words. We show that simple methods like pairwise addition or multiplication give better results than a recently proposed method that combines different types of features. The semantic relation we consider is relatedness of terms in thesauri for intellectual document classification. Thus our findings can directly be applied for the maintenance and extension of such thesauri. To the best of our knowledge this relation was not considered before in the field of distributional semantics.

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Factuality Annotation and Learning in Spanish Texts
Dina Wonsever | Aiala Rosá | Marisa Malcuori

We present a proposal for the annotation of factuality of event mentions in Spanish texts and a free available annotated corpus. Our factuality model aims to capture a pragmatic notion of factuality, trying to reflect a casual reader judgements about the realis / irrealis status of mentioned events. Also, some learning experiments (SVM and CRF) have been held, showing encouraging results.

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NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models
Frederico Tommasi Caroli | André Freitas | João Carlos Pereira da Silva | Siegfried Handschuh

Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications. These models tend to be very different from what standard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks that are not constrained by a specific kind of architecture, making it possible to use it in computational linguistics.

This paper presents some preliminary results of the OPLON project. It aimed at identifying early linguistic symptoms of cognitive decline in the elderly. This pilot study was conducted on a corpus composed of spontaneous speech sample collected from 39 subjects, who underwent a neuropsychological screening for visuo-spatial abilities, memory, language, executive functions and attention. A rich set of linguistic features was extracted from the digitalised utterances (at phonetic, suprasegmental, lexical, morphological and syntactic levels) and the statistical significance in pinpointing the pathological process was measured. Our results show remarkable trends for what concerns both the linguistic traits selection and the automatic classifiers building.

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On the Use of a Serious Game for Recording a Speech Corpus of People with Intellectual Disabilities
Mario Corrales-Astorgano | David Escudero-Mancebo | Yurena Gutiérrez-González | Valle Flores-Lucas | César González-Ferreras | Valentín Cardeñoso-Payo

This paper describes the recording of a speech corpus focused on prosody of people with intellectual disabilities. To do this, a video game is used with the aim of improving the user’s motivation. Moreover, the player’s profiles and the sentences recorded during the game sessions are described. With the purpose of identifying the main prosodic troubles of people with intellectual disabilities, some prosodic features are extracted from recordings, like fundamental frequency, energy and pauses. After that, a comparison is made between the recordings of people with intellectual disabilities and people without intellectual disabilities. This comparison shows that pauses are the best discriminative feature between these groups. To check this, a study has been done using machine learning techniques, with a classification rate superior to 80%.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that would benefit from low-cost and reliable improvements to screening and diagnosis. Human language technologies (HLTs) provide one possible route to automating a series of subjective decisions that currently inform “Gold Standard” diagnosis based on clinical judgment. In this paper, we describe a new resource to support this goal, comprised of 100 20-minute semi-structured English language samples labeled with child age, sex, IQ, autism symptom severity, and diagnostic classification. We assess the feasibility of digitizing and processing sensitive clinical samples for data sharing, and identify areas of difficulty. Using the methods described here, we propose to join forces with researchers and clinicians throughout the world to establish an international repository of annotated language samples from individuals with ASD and related disorders. This project has the potential to improve the lives of individuals with ASD and their families by identifying linguistic features that could improve remote screening, inform personalized intervention, and promote advancements in clinically-oriented HLTs.

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Vocal Pathologies Detection and Mispronounced Phonemes Identification: Case of Arabic Continuous Speech
Naim Terbeh | Mounir Zrigui

We propose in this work a novel acoustic phonetic study for Arabic people suffering from language disabilities and non-native learners of Arabic language to classify Arabic continuous speech to pathological or healthy and to identify phonemes that pose pronunciation problems (case of pathological speeches). The main idea can be summarized in comparing between the phonetic model reference to Arabic spoken language and that proper to concerned speaker. For this task, we use techniques of automatic speech processing like forced alignment and artificial neural network (ANN) (Basheer, 2000). Based on a test corpus containing 100 speech sequences, recorded by different speakers (healthy/pathological speeches and native/foreign speakers), we attain 97% as classification rate. Algorithms used in identifying phonemes that pose pronunciation problems show high efficiency: we attain an identification rate of 100%.

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Wikipedia Titles As Noun Tag Predictors
Armin Hoenen

In this paper, we investigate a covert labeling cue, namely the probability that a title (by example of the Wikipedia titles) is a noun. If this probability is very large, any list such as or comparable to the Wikipedia titles can be used as a reliable word-class (or part-of-speech tag) predictor or noun lexicon. This may be especially useful in the case of Low Resource Languages (LRL) where labeled data is lacking and putatively for Natural Language Processing (NLP) tasks such as Word Sense Disambiguation, Sentiment Analysis and Machine Translation. Profitting from the ease of digital publication on the web as opposed to print, LRL speaker communities produce resources such as Wikipedia and Wiktionary, which can be used for an assessment. We provide statistical evidence for a strong noun bias for the Wikipedia titles from 2 corpora (English, Persian) and a dictionary (Japanese) and for a typologically balanced set of 17 languages including LRLs. Additionally, we conduct a small experiment on predicting noun tags for out-of-vocabulary items in part-of-speech tagging for English.

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Japanese Word―Color Associations with and without Contexts
Jun Harashima

Although some words carry strong associations with specific colors (e.g., the word danger is associated with the color red), few studies have investigated these relationships. This may be due to the relative rarity of databases that contain large quantities of such information. Additionally, these resources are often limited to particular languages, such as English. Moreover, the existing resources often do not consider the possible contexts of words in assessing the associations between a word and a color. As a result, the influence of context on word―color associations is not fully understood. In this study, we constructed a novel language resource for word―color associations. The resource has two characteristics: First, our resource is the first to include Japanese word―color associations, which were collected via crowdsourcing. Second, the word―color associations in the resource are linked to contexts. We show that word―color associations depend on language and that associations with certain colors are affected by context information.

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The VU Sound Corpus: Adding More Fine-grained Annotations to the Freesound Database
Emiel van Miltenburg | Benjamin Timmermans | Lora Aroyo

This paper presents a collection of annotations (tags or keywords) for a set of 2,133 environmental sounds taken from the Freesound database (www.freesound.org). The annotations are acquired through an open-ended crowd-labeling task, in which participants were asked to provide keywords for each of three sounds. The main goal of this study is to find out (i) whether it is feasible to collect keywords for a large collection of sounds through crowdsourcing, and (ii) how people talk about sounds, and what information they can infer from hearing a sound in isolation. Our main finding is that it is not only feasible to perform crowd-labeling for a large collection of sounds, it is also very useful to highlight different aspects of the sounds that authors may fail to mention. Our data is freely available, and can be used to ground semantic models, improve search in audio databases, and to study the language of sound.

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Crowdsourcing a Large Dataset of Domain-Specific Context-Sensitive Semantic Verb Relations
Maria Sukhareva | Judith Eckle-Kohler | Ivan Habernal | Iryna Gurevych

We present a new large dataset of 12403 context-sensitive verb relations manually annotated via crowdsourcing. These relations capture fine-grained semantic information between verb-centric propositions, such as temporal or entailment relations. We propose a novel semantic verb relation scheme and design a multi-step annotation approach for scaling-up the annotations using crowdsourcing. We employ several quality measures and report on agreement scores. The resulting dataset is available under a permissive CreativeCommons license at www.ukp.tu-darmstadt.de/data/verb-relations/. It represents a valuable resource for various applications, such as automatic information consolidation or automatic summarization.

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Acquiring Opposition Relations among Italian Verb Senses using Crowdsourcing
Anna Feltracco | Simone Magnolini | Elisabetta Jezek | Bernardo Magnini

We describe an experiment for the acquisition of opposition relations among Italian verb senses, based on a crowdsourcing methodology. The goal of the experiment is to discuss whether the types of opposition we distinguish (i.e. complementarity, antonymy, converseness and reversiveness) are actually perceived by the crowd. In particular, we collect data for Italian by using the crowdsourcing platform CrowdFlower. We ask annotators to judge the type of opposition existing among pairs of sentences -previously judged as opposite- that differ only for a verb: the verb in the first sentence is opposite of the verb in second sentence. Data corroborate the hypothesis that some opposition relations exclude each other, while others interact, being recognized as compatible by the contributors.

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Crowdsourcing a Multi-lingual Speech Corpus: Recording, Transcription and Annotation of the CrowdIS Corpora
Andrew Caines | Christian Bentz | Calbert Graham | Tim Polzehl | Paula Buttery

We announce the release of the CROWDED CORPUS: a pair of speech corpora collected via crowdsourcing, containing a native speaker corpus of English (CROWDED_ENGLISH), and a corpus of German/English bilinguals (CROWDED_BILINGUAL). Release 1 of the CROWDED CORPUS contains 1000 recordings amounting to 33,400 tokens collected from 80 speakers and is freely available to other researchers. We recruited participants via the Crowdee application for Android. Recruits were prompted to respond to business-topic questions of the type found in language learning oral tests. We then used the CrowdFlower web application to pass these recordings to crowdworkers for transcription and annotation of errors and sentence boundaries. Finally, the sentences were tagged and parsed using standard natural language processing tools. We propose that crowdsourcing is a valid and economical method for corpus collection, and discuss the advantages and disadvantages of this approach.

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The REAL Corpus: A Crowd-Sourced Corpus of Human Generated and Evaluated Spatial References to Real-World Urban Scenes
Phil Bartie | William Mackaness | Dimitra Gkatzia | Verena Rieser

Our interest is in people’s capacity to efficiently and effectively describe geographic objects in urban scenes. The broader ambition is to develop spatial models capable of equivalent functionality able to construct such referring expressions. To that end we present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus ― Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world urban scenes together with verbal descriptions of target objects generated by humans, paired with data on how successful other people were able to identify the same object based on these descriptions. In total, the corpus contains 32 images with on average 27 descriptions per image and 3 verifications for each description. In addition, the corpus is annotated with a variety of linguistically motivated features. The paper highlights issues posed by collecting data using crowd-sourcing with an unrestricted input format, as well as using real-world urban scenes.

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Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification
Simone Hantke | Erik Marchi | Björn Schuller

Crowdsourcing is an arising collaborative approach applicable among many other applications to the area of language and speech processing. In fact, the use of crowdsourcing was already applied in the field of speech processing with promising results. However, only few studies investigated the use of crowdsourcing in computational paralinguistics. In this contribution, we propose a novel evaluator for crowdsourced-based ratings termed Weighted Trustability Evaluator (WTE) which is computed from the rater-dependent consistency over the test questions. We further investigate the reliability of crowdsourced annotations as compared to the ones obtained with traditional labelling procedures, such as constrained listening experiments in laboratories or in controlled environments. This comparison includes an in-depth analysis of obtainable classification performances. The experiments were conducted on the Speaker Likability Database (SLD) already used in the INTERSPEECH Challenge 2012, and the results lend further weight to the assumption that crowdsourcing can be applied as a reliable annotation source for computational paralinguistics given a sufficient number of raters and suited measurements of their reliability.

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Comparison of Emotional Understanding in Modality-Controlled Environments using Multimodal Online Emotional Communication Corpus
Yoshiko Arimoto | Kazuo Okanoya

In online computer-mediated communication, speakers were considered to have experienced difficulties in catching their partner’s emotions and in conveying their own emotions. To explain why online emotional communication is so difficult and to investigate how this problem should be solved, multimodal online emotional communication corpus was constructed by recording approximately 100 speakers’ emotional expressions and reactions in a modality-controlled environment. Speakers communicated over the Internet using video chat, voice chat or text chat; their face-to-face conversations were used for comparison purposes. The corpora incorporated emotional labels by evaluating the speaker’s dynamic emotional states and the measurements of the speaker’s facial expression, vocal expression and autonomic nervous system activity. For the initial study of this project, which used a large-scale emotional communication corpus, the accuracy of online emotional understanding was assessed to demonstrate the emotional labels evaluated by the speakers and to summarize the speaker’s answers on the questionnaire regarding the difference between an online chat and face-to-face conversations in which they actually participated. The results revealed that speakers have difficulty communicating their emotions in online communication environments, regardless of the type of communication modality and that inaccurate emotional understanding occurs more frequently in online computer-mediated communication than in face-to-face communication.

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Laughter in French Spontaneous Conversational Dialogs
Brigitte Bigi | Roxane Bertrand

This paper presents a quantitative description of laughter in height 1-hour French spontaneous conversations. The paper includes the raw figures for laughter as well as more details concerning inter-individual variability. It firstly describes to what extent the amount of laughter and their durations varies from speaker to speaker in all dialogs. In a second suite of analyses, this paper compares our corpus with previous analyzed corpora. In a final set of experiments, it presents some facts about overlapping laughs. This paper have quantified these all effects in free-style conversations, for the first time.

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AVAB-DBS: an Audio-Visual Affect Bursts Database for Synthesis
Kevin El Haddad | Hüseyin Çakmak | Stéphane Dupont | Thierry Dutoit

It has been shown that adding expressivity and emotional expressions to an agent’s communication systems would improve the interaction quality between this agent and a human user. In this paper we present a multimodal database of affect bursts, which are very short non-verbal expressions with facial, vocal, and gestural components that are highly synchronized and triggered by an identifiable event. This database contains motion capture and audio data of affect bursts representing disgust, startle and surprise recorded at three different levels of arousal each. This database is to be used for synthesis purposes in order to generate affect bursts of these emotions on a continuous arousal level scale.

Emotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to incorporate emotion to provide as natural an interaction as possible. To achieve said incorporation, rich sets of labeled emotional data is prerequisite. However, in Japanese, existing emotion database is still limited to unimodal and bimodal corpora. Since emotion is not only expressed through speech, but also visually at the same time, it is essential to include multiple modalities in an observation. In this paper, we present the first audio-visual emotion corpora in Japanese, collected from 14 native speakers. The corpus contains 100 minutes of annotated and transcribed material. We performed preliminary emotion recognition experiments on the corpus and achieved an accuracy of 61.42% for five classes of emotion.

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Evaluating Context Selection Strategies to Build Emotive Vector Space Models
Lucia C. Passaro | Alessandro Lenci

In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs). The system is based on the results of an existing approach that showed the possibility to create and update VSMs by exploiting crowdsourcing and human annotation. Here, we introduce a method to manipulate the contexts of the VSMs under the assumption that the emotive connotation of a target word is a function of both its syntagmatic and paradigmatic association with the various emotions. To study the differences among the proposed spaces and to confirm the reliability of the system, we report on two experiments: in the first one we validated the best candidates extracted from each model, and in the second one we compared the models’ performance on a random sample of target words. Both experiments have been implemented as crowdsourcing tasks.

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Simultaneous Sentence Boundary Detection and Alignment with Pivot-based Machine Translation Generated Lexicons
Antoine Bourlon | Chenhui Chu | Toshiaki Nakazawa | Sadao Kurohashi

Sentence alignment is a task that consists in aligning the parallel sentences in a translated article pair. This paper describes a method to perform sentence boundary detection and alignment simultaneously, which significantly improves the alignment accuracy on languages like Chinese with uncertain sentence boundaries. It relies on the definition of hard (certain) and soft (uncertain) punctuation delimiters, the latter being possibly ignored to optimize the alignment result. The alignment method is used in combination with lexicons automatically generated from the input article pairs using pivot-based MT, achieving better coverage of the input words with fewer entries than pre-existing dictionaries. Pivot-based MT makes it possible to build dictionaries for language pairs that have scarce parallel data. The alignment method is implemented in a tool that will be freely available in the near future.

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That’ll Do Fine!: A Coarse Lexical Resource for English-Hindi MT, Using Polylingual Topic Models
Diptesh Kanojia | Aditya Joshi | Pushpak Bhattacharyya | Mark James Carman

Parallel corpora are often injected with bilingual lexical resources for improved Indian language machine translation (MT). In absence of such lexical resources, multilingual topic models have been used to create coarse lexical resources in the past, using a Cartesian product approach. Our results show that for morphologically rich languages like Hindi, the Cartesian product approach is detrimental for MT. We then present a novel ‘sentential’ approach to use this coarse lexical resource from a multilingual topic model. Our coarse lexical resource when injected with a parallel corpus outperforms a system trained using parallel corpus and a good quality lexical resource. As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.

In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).

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Domain Adaptation in MT Using Titles in Wikipedia as a Parallel Corpus: Resources and Evaluation
Gorka Labaka | Iñaki Alegria | Kepa Sarasola

This paper presents how an state-of-the-art SMT system is enriched by using an extra in-domain parallel corpora extracted from Wikipedia. We collect corpora from parallel titles and from parallel fragments in comparable articles from Wikipedia. We carried out an evaluation with a double objective: evaluating the quality of the extracted data and evaluating the improvement due to the domain-adaptation. We think this can be very useful for languages with limited amount of parallel corpora, where in-domain data is crucial to improve the performance of MT sytems. The experiments on the Spanish-English language pair improve a baseline trained with the Europarl corpus in more than 2 points of BLEU when translating in the Computer Science domain.

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ProphetMT: A Tree-based SMT-driven Controlled Language Authoring/Post-Editing Tool
Xiaofeng Wu | Jinhua Du | Qun Liu | Andy Way

This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool. ProphetMT employs the source-side rules in a translation model and provides them as auto-suggestions to users. Accordingly, one might say that users are writing in a Controlled Language that is understood by the computer. ProphetMT also allows users to easily attach structural information as they compose content. When a specific rule is selected, a partial translation is promptly generated on-the-fly with the help of the structural information. Our experiments conducted on English-to-Chinese show that our proposed ProphetMT system can not only better regularise an author’s writing behaviour, but also significantly improve translation fluency which is vital to reduce the post-editing time. Additionally, when the writing and translation process is over, ProphetMT can provide an effective colour scheme to further improve the productivity of post-editors by explicitly featuring the relations between the source and target rules.

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Towards producing bilingual lexica from monolingual corpora
Jingyi Han | Núria Bel

Bilingual lexica are the basis for many cross-lingual natural language processing tasks. Recent works have shown success in learning bilingual dictionary by taking advantages of comparable corpora and a diverse set of signals derived from monolingual corpora. In the present work, we describe an approach to automatically learn bilingual lexica by training a supervised classifier using word embedding-based vectors of only a few hundred translation equivalent word pairs. The word embedding representations of translation pairs were obtained from source and target monolingual corpora, which are not necessarily related. Our classifier is able to predict whether a new word pair is under a translation relation or not. We tested it on two quite distinct language pairs Chinese-Spanish and English-Spanish. The classifiers achieved more than 0.90 precision and recall for both language pairs in different evaluation scenarios. These results show a high potential for this method to be used in bilingual lexica production for language pairs with reduced amount of parallel or comparable corpora, in particular for phrase table expansion in Statistical Machine Translation systems.

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First Steps Towards Coverage-Based Sentence Alignment
Luís Gomes | Gabriel Pereira Lopes

In this paper, we introduce a coverage-based scoring function that discriminates between parallel and non-parallel sentences. When plugged into Bleualign, a state-of-the-art sentence aligner, our function improves both precision and recall of alignments over the originally proposed BLEU score. Furthermore, since our scoring function uses Moses phrase tables directly we avoid the need to translate the texts to be aligned, which is time-consuming and a potential source of alignment errors.

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Using the TED Talks to Evaluate Spoken Post-editing of Machine Translation
Jeevanthi Liyanapathirana | Andrei Popescu-Belis

This paper presents a solution to evaluate spoken post-editing of imperfect machine translation output by a human translator. We compare two approaches to the combination of machine translation (MT) and automatic speech recognition (ASR): a heuristic algorithm and a machine learning method. To obtain a data set with spoken post-editing information, we use the French version of TED talks as the source texts submitted to MT, and the spoken English counterparts as their corrections, which are submitted to an ASR system. We experiment with various levels of artificial ASR noise and also with a state-of-the-art ASR system. The results show that the combination of MT with ASR improves over both individual outputs of MT and ASR in terms of BLEU scores, especially when ASR performance is low.

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Phrase Level Segmentation and Labelling of Machine Translation Errors
Frédéric Blain | Varvara Logacheva | Lucia Specia

This paper presents our work towards a novel approach for Quality Estimation (QE) of machine translation based on sequences of adjacent words, the so-called phrases. This new level of QE aims to provide a natural balance between QE at word and sentence-level, which are either too fine grained or too coarse levels for some applications. However, phrase-level QE implies an intrinsic challenge: how to segment a machine translation into sequence of words (contiguous or not) that represent an error. We discuss three possible segmentation strategies to automatically extract erroneous phrases. We evaluate these strategies against annotations at phrase-level produced by humans, using a new dataset collected for this purpose.

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SubCo: A Learner Translation Corpus of Human and Machine Subtitles
José Manuel Martínez Martínez | Mihaela Vela

In this paper, we present a freely available corpus of human and automatic translations of subtitles. The corpus comprises, the original English subtitles (SRC), both human (HT) and machine translations (MT) into German, as well as post-editions (PE) of the MT output. HT and MT are annotated with errors. Moreover, human evaluation is included in HT, MT, and PE. Such a corpus is a valuable resource for both human and machine translation communities, enabling the direct comparison – in terms of errors and evaluation – between human and machine translations and post-edited machine translations.

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Towards Lexical Encoding of Multi-Word Expressions in Spanish Dialects
Diana Bogantes | Eric Rodríguez | Alejandro Arauco | Alejandro Rodríguez | Agata Savary

This paper describes a pilot study in lexical encoding of multi-word expressions (MWEs) in 4 Latin American dialects of Spanish: Costa Rican, Colombian, Mexican and Peruvian. We describe the variability of MWE usage across dialects. We adapt an existing data model to a dialect-aware encoding, so as to represent dialect-related specificities, while avoiding redundancy of the data common for all dialects. A dozen of linguistic properties of MWEs can be expressed in this model, both on the level of a whole MWE and of its individual components. We describe the resulting lexical resource containing several dozens of MWEs in four dialects and we propose a method for constructing a web corpus as a support for crowdsourcing examples of MWE occurrences. The resource is available under an open license and paves the way towards a large-scale dialect-aware language resource construction, which should prove useful in both traditional and novel NLP applications.

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JATE 2.0: Java Automatic Term Extraction with Apache Solr
Ziqi Zhang | Jie Gao | Fabio Ciravegna

Automatic Term Extraction (ATE) or Recognition (ATR) is a fundamental processing step preceding many complex knowledge engineering tasks. However, few methods have been implemented as public tools and in particular, available as open-source freeware. Further, little effort is made to develop an adaptable and scalable framework that enables customization, development, and comparison of algorithms under a uniform environment. This paper introduces JATE 2.0, a complete remake of the free Java Automatic Term Extraction Toolkit (Zhang et al., 2008) delivering new features including: (1) highly modular, adaptable and scalable ATE thanks to integration with Apache Solr, the open source free-text indexing and search platform; (2) an extended collection of state-of-the-art algorithms. We carry out experiments on two well-known benchmarking datasets and compare the algorithms along the dimensions of effectiveness (precision) and efficiency (speed and memory consumption). To the best of our knowledge, this is by far the only free ATE library offering a flexible architecture and the most comprehensive collection of algorithms.

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A lexicon of perception for the identification of synaesthetic metaphors in corpora
Francesca Strik Lievers | Chu-Ren Huang

Synaesthesia is a type of metaphor associating linguistic expressions that refer to two different sensory modalities. Previous studies, based on the analysis of poetic texts, have shown that synaesthetic transfers tend to go from the lower toward the higher senses (e.g., sweet music vs. musical sweetness). In non-literary language synaesthesia is rare, and finding a sufficient number of examples manually would be too time-consuming. In order to verify whether the directionality also holds for conventional synaesthesia found in non-literary texts, an automatic procedure for the identification of instances of synaesthesia is therefore highly desirable. In this paper, we first focus on the preliminary step of this procedure, that is, the creation of a controlled lexicon of perception. Next, we present the results of a small pilot study that applies the extraction procedure to English and Italian corpus data.

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TermoPL - a Flexible Tool for Terminology Extraction
Malgorzata Marciniak | Agnieszka Mykowiecka | Piotr Rychlik

The purpose of this paper is to introduce the TermoPL tool created to extract terminology from domain corpora in Polish. The program extracts noun phrases, term candidates, with the help of a simple grammar that can be adapted for user’s needs. It applies the C-value method to rank term candidates being either the longest identified nominal phrases or their nested subphrases. The method operates on simplified base forms in order to unify morphological variants of terms and to recognize their contexts. We support the recognition of nested terms by word connection strength which allows us to eliminate truncated phrases from the top part of the term list. The program has an option to convert simplified forms of phrases into correct phrases in the nominal case. TermoPL accepts as input morphologically annotated and disambiguated domain texts and creates a list of terms, the top part of which comprises domain terminology. It can also compare two candidate term lists using three different coefficients showing asymmetry of term occurrences in this data.

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GhoSt-NN: A Representative Gold Standard of German Noun-Noun Compounds
Sabine Schulte im Walde | Anna Hätty | Stefan Bott | Nana Khvtisavrishvili

This paper presents a novel gold standard of German noun-noun compounds (Ghost-NN) including 868 compounds annotated with corpus frequencies of the compounds and their constituents, productivity and ambiguity of the constituents, semantic relations between the constituents, and compositionality ratings of compound-constituent pairs. Moreover, a subset of the compounds containing 180 compounds is balanced for the productivity of the modifiers (distinguishing low/mid/high productivity) and the ambiguity of the heads (distinguishing between heads with 1, 2 and >2 senses

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DeQue: A Lexicon of Complex Prepositions and Conjunctions in French
Carlos Ramisch | Alexis Nasr | André Valli | José Deulofeu

We introduce DeQue, a lexicon covering French complex prepositions (CPRE) like “à partir de” (from) and complex conjunctions (CCONJ) like “bien que” (although). The lexicon includes fine-grained linguistic description based on empirical evidence. We describe the general characteristics of CPRE and CCONJ in French, with special focus on syntactic ambiguity. Then, we list the selection criteria used to build the lexicon and the corpus-based methodology employed to collect entries. Finally, we quantify the ambiguity of each construction by annotating around 100 sentences randomly taken from the FRWaC. In addition to its theoretical value, the resource has many potential practical applications. We intend to employ DeQue for treebank annotation and to train a dependency parser that can takes complex constructions into account.

This paper summarizes the preliminary results of an ongoing survey on multiword resources carried out within the IC1207 Cost Action PARSEME (PARSing and Multi-word Expressions). Despite the availability of language resource catalogs and the inventory of multiword datasets on the SIGLEX-MWE website, multiword resources are scattered and difficult to find. In many cases, language resources such as corpora, treebanks, or lexical databases include multiwords as part of their data or take them into account in their annotations. However, these resources need to be centralized to make them accessible. The aim of this survey is to create a portal where researchers can easily find multiword(-aware) language resources for their research. We report on the design of the survey and analyze the data gathered so far. We also discuss the problems we have detected upon examination of the data as well as possible ways of enhancing the survey.

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Multiword Expressions in Child Language
Rodrigo Wilkens | Marco Idiart | Aline Villavicencio

The goal of this work is to introduce CHILDES-MWE, which contains English CHILDES corpora automatically annotated with Multiword Expressions (MWEs) information. The result is a resource with almost 350,000 sentences annotated with more than 70,000 distinct MWEs of various types from both longitudinal and latitudinal corpora. This resource can be used for large scale language acquisition studies of how MWEs feature in child language. Focusing on compound nouns (CN), we then verify in a longitudinal study if there are differences in the distribution and compositionality of CNs in child-directed and child-produced sentences across ages. Moreover, using additional latitudinal data, we investigate if there are further differences in CN usage and in compositionality preferences. The results obtained for the child-produced sentences reflect CN distribution and compositionality in child-directed sentences.

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Transfer-Based Learning-to-Rank Assessment of Medical Term Technicality
Dhouha Bouamor | Leonardo Campillos Llanos | Anne-Laure Ligozat | Sophie Rosset | Pierre Zweigenbaum

While measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In this paper, we train a learning-to-rank model to determine a specialization degree for each term found in a given list. Since no training data for this task exist for French, we train our system with non-lexical features on English data, namely, the Consumer Health Vocabulary, then apply it to French. The features include the likelihood ratio of the term based on specialized and lay language models, and tests for containing morphologically complex words. The evaluation of this approach is conducted on 134 terms from the UMLS Metathesaurus and 868 terms from the Eugloss thesaurus. The Normalized Discounted Cumulative Gain obtained by our system is over 0.8 on both test sets. Besides, thanks to the learning-to-rank approach, adding morphological features to the language model features improves the results on the Eugloss thesaurus.

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Example-based Acquisition of Fine-grained Collocation Resources
Sara Rodríguez-Fernández | Roberto Carlini | Luis Espinosa Anke | Leo Wanner

Collocations such as “heavy rain” or “make [a] decision”, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate to express a particular meaning, e.g., both “heavy” and “strong” express the meaning ‘intense’, but while “rain” selects “heavy”, “wind” selects “strong”. Lexical Functions (LFs) describe the meanings that hold between the elements of collocations, such as ‘intense’, ‘perform’, ‘create’, ‘increase’, etc. Language resources with semantically classified collocations would be of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised approach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which, given an example of a collocation for each of the target LFs and a set of bases, the system retrieves a list of collocates for each base and LF.

By means of an online survey, we have investigated ways in which various types of multiword expressions are annotated in existing treebanks. The results indicate that there is considerable variation in treatments across treebanks and thereby also, to some extent, across languages and across theoretical frameworks. The comparison is focused on the annotation of light verb constructions and verbal idioms. The survey shows that the light verb constructions either get special annotations as such, or are treated as ordinary verbs, while VP idioms are handled through different strategies. Based on insights from our investigation, we propose some general guidelines for annotating multiword expressions in treebanks. The recommendations address the following application-based needs: distinguishing MWEs from similar but compositional constructions; searching distinct types of MWEs in treebanks; awareness of literal and nonliteral meanings; and normalization of the MWE representation. The cross-lingually and cross-theoretically focused survey is intended as an aid to accessing treebanks and an aid for further work on treebank annotation.

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Multiword Expressions Dataset for Indian Languages
Dhirendra Singh | Sudha Bhingardive | Pushpak Bhattacharyya

Multiword Expressions (MWEs) are used frequently in natural languages, but understanding the diversity in MWEs is one of the open problem in the area of Natural Language Processing. In the context of Indian languages, MWEs play an important role. In this paper, we present MWEs annotation dataset created for Indian languages viz., Hindi and Marathi. We extract possible MWE candidates using two repositories: 1) the POS-tagged corpus and 2) the IndoWordNet synsets. Annotation is done for two types of MWEs: compound nouns and light verb constructions. In the process of annotation, human annotators tag valid MWEs from these candidates based on the standard guidelines provided to them. We obtained 3178 compound nouns and 2556 light verb constructions in Hindi and 1003 compound nouns and 2416 light verb constructions in Marathi using two repositories mentioned before. This created resource is made available publicly and can be used as a gold standard for Hindi and Marathi MWE systems.

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MarsaGram: an excursion in the forests of parsing trees
Philippe Blache | Stéphane Rauzy | Grégoire Montcheuil

The question of how to compare languages and more generally the domain of linguistic typology, relies on the study of different linguistic properties or phenomena. Classically, such a comparison is done semi-manually, for example by extracting information from databases such as the WALS. However, it remains difficult to identify precisely regular parameters, available for different languages, that can be used as a basis towards modeling. We propose in this paper, focusing on the question of syntactic typology, a method for automatically extracting such parameters from treebanks, bringing them into a typology perspective. We present the method and the tools for inferring such information and navigating through the treebanks. The approach has been applied to 10 languages of the Universal Dependencies Treebank. We approach is evaluated by showing how automatic classification correlates with language families.

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EasyTree: A Graphical Tool for Dependency Tree Annotation
Alexa Little | Stephen Tratz

This paper introduces EasyTree, a dynamic graphical tool for dependency tree annotation. Built in JavaScript using the popular D3 data visualization library, EasyTree allows annotators to construct and label trees entirely by manipulating graphics, and then export the corresponding data in JSON format. Human users are thus able to annotate in an intuitive way without compromising the machine-compatibility of the output. EasyTree has a number of features to assist annotators, including color-coded part-of-speech indicators and optional translation displays. It can also be customized to suit a wide range of projects; part-of-speech categories, edge labels, and many other settings can be edited from within the GUI. The system also utilizes UTF-8 encoding and properly handles both left-to-right and right-to-left scripts. By providing a user-friendly annotation tool, we aim to reduce time spent transforming data or learning to use the software, to improve the user experience for annotators, and to make annotation approachable even for inexperienced users. Unlike existing solutions, EasyTree is built entirely with standard web technologies–JavaScript, HTML, and CSS–making it ideal for web-based annotation efforts, including crowdsourcing efforts.

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Hypergraph Modelization of a Syntactically Annotated English Wikipedia Dump
Edmundo Pavel Soriano Morales | Julien Ah-Pine | Sabine Loudcher

Wikipedia, the well known internet encyclopedia, is nowadays a widely used source of information. To leverage its rich information, already parsed versions of Wikipedia have been proposed. We present an annotated dump of the English Wikipedia. This dump draws upon previously released Wikipedia parsed dumps. Still, we head in a different direction. In this parse we focus more into the syntactical characteristics of words: aside from the classical Part-of-Speech (PoS) tags and dependency parsing relations, we provide the full constituent parse branch for each word in a succinct way. Additionally, we propose a hypergraph network representation of the extracted linguistic information. The proposed modelization aims to take advantage of the information stocked within our parsed Wikipedia dump. We hope that by releasing these resources, researchers from the concerned communities will have a ready-to-experiment Wikipedia corpus to compare and distribute their work. We render public our parsed Wikipedia dump as well as the tool (and its source code) used to perform the parse. The hypergraph network and its related metadata is also distributed.

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Detecting Annotation Scheme Variation in Out-of-Domain Treebanks
Yannick Versley | Julius Steen

To ensure portability of NLP systems across multiple domains, existing treebanks are often extended by adding trees from interesting domains that were not part of the initial annotation effort. In this paper, we will argue that it is both useful from an application viewpoint and enlightening from a linguistic viewpoint to detect and reduce divergence in annotation schemes between extant and new parts in a set of treebanks that is to be used in evaluation experiments. The results of our correction and harmonization efforts will be made available to the public as a test suite for the evaluation of constituent parsing.

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Universal Dependencies for Persian
Mojgan Seraji | Filip Ginter | Joakim Nivre

The Persian Universal Dependency Treebank (Persian UD) is a recent effort of treebanking Persian with Universal Dependencies (UD), an ongoing project that designs unified and cross-linguistically valid grammatical representations including part-of-speech tags, morphological features, and dependency relations. The Persian UD is the converted version of the Uppsala Persian Dependency Treebank (UPDT) to the universal dependencies framework and consists of nearly 6,000 sentences and 152,871 word tokens with an average sentence length of 25 words. In addition to the universal dependencies syntactic annotation guidelines, the two treebanks differ in tokenization. All words containing unsegmented clitics (pronominal and copula clitics) annotated with complex labels in the UPDT have been separated from the clitics and appear with distinct labels in the Persian UD. The treebank has its original syntactic annotation scheme based on Stanford Typed Dependencies. In this paper, we present the approaches taken in the development of the Persian UD.

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Hard Time Parsing Questions: Building a QuestionBank for French
Djamé Seddah | Marie Candito

We present the French Question Bank, a treebank of 2600 questions. We show that classical parsing model performance drop while the inclusion of this data set is highly beneficial without harming the parsing of non-question data. when facing out-of- domain data with strong structural diver- gences. Two thirds being aligned with the QB (Judge et al., 2006) and being freely available, this treebank will prove useful to build robust NLP systems.

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Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks
Sebastian Schuster | Christopher D. Manning

Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also defines two dependency graph representations which contain additional and augmented relations that explicitly capture otherwise implicit relations between content words. In this paper, we revisit and extend these dependency graph representations in light of the recent Universal Dependencies (UD) initiative and provide a detailed account of an enhanced and an enhanced++ English UD representation. We further present a converter from constituency to basic, i.e., strict surface structure, UD trees, and a converter from basic UD trees to enhanced and enhanced++ English UD graphs. We release both converters as part of Stanford CoreNLP and the Stanford Parser.

This paper describes our efforts for the development of a Proposition Bank for Urdu, an Indo-Aryan language. Our primary goal is the labeling of syntactic nodes in the existing Urdu dependency Treebank with specific argument labels. In essence, it involves annotation of predicate argument structures of both simple and complex predicates in the Treebank corpus. We describe the overall process of building the PropBank of Urdu. We discuss various statistics pertaining to the Urdu PropBank and the issues which the annotators encountered while developing the PropBank. We also discuss how these challenges were addressed to successfully expand the PropBank corpus. While reporting the Inter-annotator agreement between the two annotators, we show that the annotators share similar understanding of the annotation guidelines and of the linguistic phenomena present in the language. The present size of this Propbank is around 180,000 tokens which is double-propbanked by the two annotators for simple predicates. Another 100,000 tokens have been annotated for complex predicates of Urdu.

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Czech Legal Text Treebank 1.0
Vincent Kríž | Barbora Hladká | Zdeňka Urešová

We introduce a new member of the family of Prague dependency treebanks. The Czech Legal Text Treebank 1.0 is a morphologically and syntactically annotated corpus of 1,128 sentences. The treebank contains texts from the legal domain, namely the documents from the Collection of Laws of the Czech Republic. Legal texts differ from other domains in several language phenomena influenced by rather high frequency of very long sentences. A manual annotation of such sentences presents a new challenge. We describe a strategy and tools for this task. The resulting treebank can be explored in various ways. It can be downloaded from the LINDAT/CLARIN repository and viewed locally using the TrEd editor or it can be accessed on-line using the KonText and TreeQuery tools.

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Concepticon: A Resource for the Linking of Concept Lists
Johann-Mattis List | Michael Cysouw | Robert Forkel

We present an attempt to link the large amount of different concept lists which are used in the linguistic literature, ranging from Swadesh lists in historical linguistics to naming tests in clinical studies and psycholinguistics. This resource, our Concepticon, links 30 222 concept labels from 160 conceptlists to 2495 concept sets. Each concept set is given a unique identifier, a unique label, and a human-readable definition. Concept sets are further structured by defining different relations between the concepts. The resource can be used for various purposes. Serving as a rich reference for new and existing databases in diachronic and synchronic linguistics, it allows researchers a quick access to studies on semantic change, cross-linguistic polysemies, and semantic associations.

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LVF-lemon ― Towards a Linked Data Representation of “Les Verbes français”
Ingrid Falk | Achim Stein

In this study we elaborate a road map for the conversion of a traditional lexical syntactico-semantic resource for French into a linguistic linked open data (LLOD) model. Our approach uses current best-practices and the analyses of earlier similar undertakings (lemonUBY and PDEV-lemon) to tease out the most appropriate representation for our resource.

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Riddle Generation using Word Associations
Paloma Galván | Virginia Francisco | Raquel Hervás | Gonzalo Méndez

In knowledge bases where concepts have associated properties, there is a large amount of comparative information that is implicitly encoded in the values of the properties these concepts share. Although there have been previous approaches to generating riddles, none of them seem to take advantage of structured information stored in knowledge bases such as Thesaurus Rex, which organizes concepts according to the fine grained ad-hoc categories they are placed into by speakers in everyday language, along with associated properties or modifiers. Taking advantage of these shared properties, we have developed a riddle generator that creates riddles about concepts represented as common nouns. The base of these riddles are comparisons between the target concept and other entities that share some of its properties. In this paper, we describe the process we have followed to generate the riddles starting from the target concept and we show the results of the first evaluation we have carried out to test the quality of the resulting riddles.

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Challenges of Adjective Mapping between plWordNet and Princeton WordNet
Ewa Rudnicka | Wojciech Witkowski | Katarzyna Podlaska

The paper presents the strategy and results of mapping adjective synsets between plWordNet (the wordnet of Polish, cf. Piasecki et al. 2009, Maziarz et al. 2013) and Princeton WordNet (cf. Fellbaum 1998). The main challenge of this enterprise has been very different synset relation structures in the two networks: horizontal, dumbbell-model based in PWN and vertical, hyponymy-based in plWN. Moreover, the two wordnets display differences in the grouping of adjectives into semantic domains and in the size of the adjective category. The handle the above contrasts, a series of automatic prompt algorithms and a manual mapping procedure relying on corresponding synset and lexical unit relations as well as on inter-lingual relations between noun synsets were proposed in the pilot stage of mapping (Rudnicka et al. 2015). In the paper we discuss the final results of the mapping process as well as explain example mapping choices. Suggestions for further development of mapping are also given.

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Relation- and Phrase-level Linking of FrameNet with Sar-graphs
Aleksandra Gabryszak | Sebastian Krause | Leonhard Hennig | Feiyu Xu | Hans Uszkoreit

Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and illustrate the benefits of the linked data in a relation extraction setting. While FrameNet consists of schematic representations of situations, linked to lexemes and their valency patterns, sar-graphs are knowledge resources that connect semantic relations from factual knowledge graphs to the linguistic phrases used to express instances of these relations. We analyze the conceptual similarities and differences of both resources and propose to link sar-graphs and FrameNet on the levels of relations/frames as well as phrases. The former alignment involves a manual ontology mapping step, which allows us to extend sar-graphs with new phrase patterns from FrameNet. The phrase-level linking, on the other hand, is fully automatic. We investigate the quality of the automatically constructed links and identify two main classes of errors.

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Mapping Ontologies Using Ontologies: Cross-lingual Semantic Role Information Transfer
Balázs Indig | Márton Miháltz | András Simonyi

This paper presents the process of enriching the verb frame database of a Hungarian natural language parser to enable the assignment of semantic roles. We accomplished this by linking the parser’s verb frame database to existing linguistic resources such as VerbNet and WordNet, and automatically transferring back semantic knowledge. We developed OWL ontologies that map the various constraint description formalisms of the linked resources and employed a logical reasoning device to facilitate the linking procedure. We present results and discuss the challenges and pitfalls that arose from this undertaking.

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Generating a Large-Scale Entity Linking Dictionary from Wikipedia Link Structure and Article Text
Ravindra Harige | Paul Buitelaar

Wikipedia has been increasingly used as a knowledge base for open-domain Named Entity Linking and Disambiguation. In this task, a dictionary with entity surface forms plays an important role in finding a set of candidate entities for the mentions in text. Existing dictionaries mostly rely on the Wikipedia link structure, like anchor texts, redirect links and disambiguation links. In this paper, we introduce a dictionary for Entity Linking that includes name variations extracted from Wikipedia article text, in addition to name variations derived from the Wikipedia link structure. With this approach, we show an increase in the coverage of entities and their mentions in the dictionary in comparison to other Wikipedia based dictionaries.

The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data.

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Cross-lingual RDF Thesauri Interlinking
Tatiana Lesnikova | Jérôme David | Jérôme Euzenat

Various lexical resources are being published in RDF. To enhance the usability of these resources, identical resources in different data sets should be linked. If lexical resources are described in different natural languages, then techniques to deal with multilinguality are required for interlinking. In this paper, we evaluate machine translation for interlinking concepts, i.e., generic entities named with a common noun or term. In our previous work, the evaluated method has been applied on named entities. We conduct two experiments involving different thesauri in different languages. The first experiment involves concepts from the TheSoz multilingual thesaurus in three languages: English, French and German. The second experiment involves concepts from the EuroVoc and AGROVOC thesauri in English and Chinese respectively. Our results demonstrate that machine translation can be beneficial for cross-lingual thesauri interlinking independently of a dataset structure.

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The Language Resource Life Cycle: Towards a Generic Model for Creating, Maintaining, Using and Distributing Language Resources
Georg Rehm

Language Resources (LRs) are an essential ingredient of current approaches in Linguistics, Computational Linguistics, Language Technology and related fields. LRs are collections of spoken or written language data, typically annotated with linguistic analysis information. Different types of LRs exist, for example, corpora, ontologies, lexicons, collections of spoken language data (audio), or collections that also include video (multimedia, multimodal). Often, LRs are distributed with specific tools, documentation, manuals or research publications. The different phases that involve creating and distributing an LR can be conceptualised as a life cycle. While the idea of handling the LR production and maintenance process in terms of a life cycle has been brought up quite some time ago, a best practice model or common approach can still be considered a research gap. This article wants to help fill this gap by proposing an initial version of a generic Language Resource Life Cycle that can be used to inform, direct, control and evaluate LR research and development activities (including description, management, production, validation and evaluation workflows).

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A Large-scale Recipe and Meal Data Collection as Infrastructure for Food Research
Jun Harashima | Michiaki Ariga | Kenta Murata | Masayuki Ioki

Everyday meals are an important part of our daily lives and, currently, there are many Internet sites that help us plan these meals. Allied to the growth in the amount of food data such as recipes available on the Internet is an increase in the number of studies on these data, such as recipe analysis and recipe search. However, there are few publicly available resources for food research; those that do exist do not include a wide range of food data or any meal data (that is, likely combinations of recipes). In this study, we construct a large-scale recipe and meal data collection as the underlying infrastructure to promote food research. Our corpus consists of approximately 1.7 million recipes and 36000 meals in cookpad, one of the largest recipe sites in the world. We made the corpus available to researchers in February 2015 and as of February 2016, 82 research groups at 56 universities have made use of it to enhance their studies.

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EstNLTK - NLP Toolkit for Estonian
Siim Orasmaa | Timo Petmanson | Alexander Tkachenko | Sven Laur | Heiki-Jaan Kaalep

Although there are many tools for natural language processing tasks in Estonian, these tools are very loosely interoperable, and it is not easy to build practical applications on top of them. In this paper, we introduce a new Python library for natural language processing in Estonian, which provides unified programming interface for various NLP components. The EstNLTK toolkit provides utilities for basic NLP tasks including tokenization, morphological analysis, lemmatisation and named entity recognition as well as offers more advanced features such as a clause segmentation, temporal expression extraction and normalization, verb chain detection, Estonian Wordnet integration and rule-based information extraction. Accompanied by a detailed API documentation and comprehensive tutorials, EstNLTK is suitable for a wide range of audience. We believe EstNLTK is mature enough to be used for developing NLP-backed systems both in industry and research. EstNLTK is freely available under the GNU GPL version 2+ license, which is standard for academic software.

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South African National Centre for Digital Language Resources
Justus Roux

This presentation introduces the imminent establishment of a new language resource infrastructure focusing on languages spoken in Southern Africa, with an eventual aim to become a hub for digital language resources within Sub-Saharan Africa. The Constitution of South Africa makes provision for 11 official languages all with equal status. The current language Resource Management Agency will be merged with the new Centre, which will have a wider focus than that of data acquisition, management and distribution. The Centre will entertain two main programs: Digitisation and Digital Humanities. The digitisation program will focus on the systematic digitisation of relevant text, speech and multi-modal data across the official languages. Relevancy will be determined by a Scientific Advisory Board. This will take place on a continuous basis through specified projects allocated to national members of the Centre, as well as through open-calls aimed at the academic as well as local communities. The digital resources will be managed and distributed through a dedicated web-based portal. The development of the Digital Humanities program will entail extensive academic support for projects implementing digital language based data. The Centre will function as an enabling research infrastructure primarily supported by national government and hosted by the North-West University.

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Design and Development of the MERLIN Learner Corpus Platform
Verena Lyding | Karin Schöne

In this paper, we report on the design and development of an online search platform for the MERLIN corpus of learner texts in Czech, German and Italian. It was created in the context of the MERLIN project, which aims at empirically illustrating features of the Common European Framework of Reference (CEFR) for evaluating language competences based on authentic learner text productions compiled into a learner corpus. Furthermore, the project aims at providing access to the corpus through a search interface adapted to the needs of multifaceted target groups involved with language learning and teaching. This article starts by providing a brief overview on the project ambition, the data resource and its intended target groups. Subsequently, the main focus of the article is on the design and development process of the platform, which is carried out in a user-centred fashion. The paper presents the user studies carried out to collect requirements, details the resulting decisions concerning the platform design and its implementation, and reports on the evaluation of the platform prototype and final adjustments.

Language resources are valuable assets, both for institutions and researchers. To safeguard these resources requirements for repository systems and data management have been specified by various branch organizations, e.g., CLARIN and the Data Seal of Approval. This paper describes these and some additional ones posed by the authors’ home institutions. And it shows how they are met by FLAT, to provide a new home for language resources. The basis of FLAT is formed by the Fedora Commons repository system. This repository system can meet many of the requirements out-of-the box, but still additional configuration and some development work is needed to meet the remaining ones, e.g., to add support for Handles and Component Metadata. This paper describes design decisions taken in the construction of FLAT’s system architecture via a mix-and-match strategy, with a preference for the reuse of existing solutions. FLAT is developed and used by the Meertens Institute and The Language Archive, but is also freely available for anyone in need of a CLARIN-compliant repository for their language resources.

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CLARIAH in the Netherlands
Jan Odijk

I introduce CLARIAH in the Netherlands, which aims to contribute the Netherlands part of a Europe-wide humanities research infrastructure. I describe the digital turn in the humanities, the background and context of CLARIAH, both nationally and internationally, its relation to the CLARIN and DARIAH infrastructures, and the rationale for joining forces between CLARIN and DARIAH in the Netherlands. I also describe the first results of joining forces as achieved in the CLARIAH-SEED project, and the plans of the CLARIAH-CORE project, which is currently running

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Crosswalking from CMDI to Dublin Core and MARC 21
Claus Zinn | Thorsten Trippel | Steve Kaminski | Emanuel Dima

The Component MetaData Infrastructure (CMDI) is a framework for the creation and usage of metadata formats to describe all kinds of resources in the CLARIN world. To better connect to the library world, and to allow librarians to enter metadata for linguistic resources into their catalogues, a crosswalk from CMDI-based formats to bibliographic standards is required. The general and rather fluid nature of CMDI, however, makes it hard to map arbitrary CMDI schemas to metadata standards such as Dublin Core (DC) or MARC 21, which have a mature, well-defined and fixed set of field descriptors. In this paper, we address the issue and propose crosswalks between CMDI-based profiles originating from the NaLiDa project and DC and MARC 21, respectively.

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Data Management Plans and Data Centers
Denise DiPersio | Christopher Cieri | Daniel Jaquette

Data management plans, data sharing plans and the like are now required by funders worldwide as part of research proposals. Concerned with promoting the notion of open scientific data, funders view such plans as the framework for satisfying the generally accepted requirements for data generated in funded research projects, among them that it be accessible, usable, standardized to the degree possible, secure and stable. This paper examines the origins of data management plans, their requirements and issues they raise for data centers and HLT resource development in general.

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UIMA-Based JCoRe 2.0 Goes GitHub and Maven Central ― State-of-the-Art Software Resource Engineering and Distribution of NLP Pipelines
Udo Hahn | Franz Matthies | Erik Faessler | Johannes Hellrich

We introduce JCoRe 2.0, the relaunch of a UIMA-based open software repository for full-scale natural language processing originating from the Jena University Language & Information Engineering (JULIE) Lab. In an attempt to put the new release of JCoRe on firm software engineering ground, we uploaded it to GitHub, a social coding platform, with an underlying source code versioning system and various means to support collaboration for software development and code modification management. In order to automate the builds of complex NLP pipelines and properly represent and track dependencies of the underlying Java code, we incorporated Maven as part of our software configuration management efforts. In the meantime, we have deployed our artifacts on Maven Central, as well. JCoRe 2.0 offers a broad range of text analytics functionality (mostly) for English-language scientific abstracts and full-text articles, especially from the life sciences domain.

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Facilitating Metadata Interoperability in CLARIN-DK
Lene Offersgaard | Dorte Haltrup Hansen

The issue for CLARIN archives at the metadata level is to facilitate the user’s possibility to describe their data, even with their own standard, and at the same time make these metadata meaningful for a variety of users with a variety of resource types, and ensure that the metadata are useful for search across all resources both at the national and at the European level. We see that different people from different research communities fill in the metadata in different ways even though the metadata was defined and documented. This has impacted when the metadata are harvested and displayed in different environments. A loss of information is at stake. In this paper we view the challenges of ensuring metadata interoperability through examples of propagation of metadata values from the CLARIN-DK archive to the VLO. We see that the CLARIN Community in many ways support interoperability, but argue that agreeing upon standards, making clear definitions of the semantics of the metadata and their content is inevitable for the interoperability to work successfully. The key points are clear and freely available definitions, accessible documentation and easily usable facilities and guidelines for the metadata creators.

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The IPR-cleared Corpus of Contemporary Written and Spoken Romanian Language
Dan Tufiș | Verginica Barbu Mititelu | Elena Irimia | Ștefan Daniel Dumitrescu | Tiberiu Boroș

The article describes the current status of a large national project, CoRoLa, aiming at building a reference corpus for the contemporary Romanian language. Unlike many other national corpora, CoRoLa contains only - IPR cleared texts and speech data, obtained from some of the country’s most representative publishing houses, broadcasting agencies, editorial offices, newspapers and popular bloggers. For the written component 500 million tokens are targeted and for the oral one 300 hours of recordings. The choice of texts is done according to their functional style, domain and subdomain, also with an eye to the international practice. A metadata file (following the CMDI model) is associated to each text file. Collected texts are cleaned and transformed in a format compatible with the tools for automatic processing (segmentation, tokenization, lemmatization, part-of-speech tagging). The paper also presents up-to-date statistics about the structure of the corpus almost two years before its official launching. The corpus will be freely available for searching. Users will be able to download the results of their searches and those original files when not against stipulations in the protocols we have with text providers.

The paper concentrates on the design, composition and annotation of SYN2015, a new 100-million representative corpus of contemporary written Czech. SYN2015 is a sequel of the representative corpora of the SYN series that can be described as traditional (as opposed to the web-crawled corpora), featuring cleared copyright issues, well-defined composition, reliability of annotation and high-quality text processing. At the same time, SYN2015 is designed as a reflection of the variety of written Czech text production with necessary methodological and technological enhancements that include a detailed bibliographic annotation and text classification based on an updated scheme. The corpus has been produced using a completely rebuilt text processing toolchain called SynKorp. SYN2015 is lemmatized, morphologically and syntactically annotated with state-of-the-art tools. It has been published within the framework of the Czech National Corpus and it is available via the standard corpus query interface KonText at http://kontext.korpus.cz as well as a dataset in shuffled format.

This proposal describes a new way to visualise resources in the LREMap, a community-built repository of language resource descriptions and uses. The LREMap is represented as a force-directed graph, where resources, papers and authors are nodes. The analysis of the visual representation of the underlying graph is used to study how the community gathers around LRs and how LRs are used in research.

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The Public License Selector: Making Open Licensing Easier
Pawel Kamocki | Pavel Straňák | Michal Sedlák

Researchers in Natural Language Processing rely on availability of data and software, ideally under open licenses, but little is done to actively encourage it. In fact, the current Copyright framework grants exclusive rights to authors to copy their works, make them available to the public and make derivative works (such as annotated language corpora). Moreover, in the EU databases are protected against unauthorized extraction and re-utilization of their contents. Therefore, proper public licensing plays a crucial role in providing access to research data. A public license is a license that grants certain rights not to one particular user, but to the general public (everybody). Our article presents a tool that we developed and whose purpose is to assist the user in the licensing process. As software and data should be licensed under different licenses, the tool is composed of two separate parts: Data and Software. The underlying logic as well as elements of the graphic interface are presented below.

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NLP Infrastructure for the Lithuanian Language
Daiva Vitkutė-Adžgauskienė | Andrius Utka | Darius Amilevičius | Tomas Krilavičius

The Information System for Syntactic and Semantic Analysis of the Lithuanian language (lith. Lietuvių kalbos sintaksinės ir semantinės analizės informacinė sistema, LKSSAIS) is the first infrastructure for the Lithuanian language combining Lithuanian language tools and resources for diverse linguistic research and applications tasks. It provides access to the basic as well as advanced natural language processing tools and resources, including tools for corpus creation and management, text preprocessing and annotation, ontology building, named entity recognition, morphosyntactic and semantic analysis, sentiment analysis, etc. It is an important platform for researchers and developers in the field of natural language technology.

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CodE Alltag: A German-Language E-Mail Corpus
Ulrike Krieg-Holz | Christian Schuschnig | Franz Matthies | Benjamin Redling | Udo Hahn

We introduce CODE ALLTAG, a text corpus composed of German-language e-mails. It is divided into two partitions: the first of these portions, CODE ALLTAG_XL, consists of a bulk-size collection drawn from an openly accessible e-mail archive (roughly 1.5M e-mails), whereas the second portion, CODE ALLTAG_S+d, is much smaller in size (less than thousand e-mails), yet excels with demographic data from each author of an e-mail. CODE ALLTAG, thus, currently constitutes the largest E-Mail corpus ever built. In this paper, we describe, for both parts, the solicitation process for gathering e-mails, present descriptive statistical properties of the corpus, and, for CODE ALLTAG_S+d, reveal a compilation of demographic features of the donors of e-mails.

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Rapid Development of Morphological Analyzers for Typologically Diverse Languages
Seth Kulick | Ann Bies

The Low Resource Language research conducted under DARPA’s Broad Operational Language Translation (BOLT) program required the rapid creation of text corpora of typologically diverse languages (Turkish, Hausa, and Uzbek) which were annotated with morphological information, along with other types of annotation. Since the output of morphological analyzers is a significant aid to morphological annotation, we developed a morphological analyzer for each language in order to support the annotation task, and also as a deliverable by itself. Our framework for analyzer creation results in tables similar to those used in the successful SAMA analyzer for Arabic, but with a more abstract linguistic level, from which the tables are derived. A lexicon was developed from available resources for integration with the analyzer, and given the speed of development and uncertain coverage of the lexicon, we assumed that the analyzer would necessarily be lacking in some coverage for the project annotation. Our analyzer framework was therefore focused on rapid implementation of the key structures of the language, together with accepting “wildcard” solutions as possible analyses for a word with an unknown stem, building upon our similar experiences with morphological annotation with Modern Standard Arabic and Egyptian Arabic.

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A Neural Lemmatizer for Bengali
Abhisek Chakrabarty | Akshay Chaturvedi | Utpal Garain

We propose a novel neural lemmatization model which is language independent and supervised in nature. To handle the words in a neural framework, word embedding technique is used to represent words as vectors. The proposed lemmatizer makes use of contextual information of the surface word to be lemmatized. Given a word along with its contextual neighbours as input, the model is designed to produce the lemma of the concerned word as output. We introduce a new network architecture that permits only dimension specific connections between the input and the output layer of the model. For the present work, Bengali is taken as the reference language. Two datasets are prepared for training and testing purpose consisting of 19,159 and 2,126 instances respectively. As Bengali is a resource scarce language, these datasets would be beneficial for the respective research community. Evaluation method shows that the neural lemmatizer achieves 69.57% accuracy on the test dataset and outperforms the simple cosine similarity based baseline strategy by a margin of 1.37%.

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A Finite-state Morphological Analyser for Tuvan
Francis Tyers | Aziyana Bayyr-ool | Aelita Salchak | Jonathan Washington

~This paper describes the development of free/open-source finite-state morphological transducers for Tuvan, a Turkic language spoken in and around the Tuvan Republic in Russia. The finite-state toolkit used for the work is the Helsinki Finite-State Toolkit (HFST), we use the lexc formalism for modelling the morphotactics and twol formalism for modelling morphophonological alternations. We present a novel description of the morphological combinatorics of pseudo-derivational morphemes in Tuvan. An evaluation is presented which shows that the transducer has a reasonable coverage―around 93%―on freely-available corpora of the languages, and high precision―over 99%―on a manually verified test set.

We describe an extensive and versatile lexical resource for Latvian, an under-resourced Indo-European language, which we call Tezaurs (Latvian for ‘thesaurus’). It comprises a large explanatory dictionary of more than 250,000 entries that are derived from more than 280 external sources. The dictionary is enriched with phonetic, morphological, semantic and other annotations, as well as augmented by various language processing tools allowing for the generation of inflectional forms and pronunciation, for on-the-fly selection of corpus examples, for suggesting synonyms, etc. Tezaurs is available as a public and widely used web application for end-users, as an open data set for the use in language technology (LT), and as an API ― a set of web services for the integration into third-party applications. The ultimate goal of Tezaurs is to be the central computational lexicon for Latvian, bringing together all Latvian words and frequently used multi-word units and allowing for the integration of other LT resources and tools.

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A Finite-State Morphological Analyser for Sindhi
Raveesh Motlani | Francis Tyers | Dipti Sharma

Morphological analysis is a fundamental task in natural-language processing, which is used in other NLP applications such as part-of-speech tagging, syntactic parsing, information retrieval, machine translation, etc. In this paper, we present our work on the development of free/open-source finite-state morphological analyser for Sindhi. We have used Apertium’s lttoolbox as our finite-state toolkit to implement the transducer. The system is developed using a paradigm-based approach, wherein a paradigm defines all the word forms and their morphological features for a given stem (lemma). We have evaluated our system on the Sindhi Wikipedia corpus and achieved a reasonable coverage of 81% and a precision of over 97%.

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Deriving Morphological Analyzers from Example Inflections
Markus Forsberg | Mans Hulden

This paper presents a semi-automatic method to derive morphological analyzers from a limited number of example inflections suitable for languages with alphabetic writing systems. The system we present learns the inflectional behavior of morphological paradigms from examples and converts the learned paradigms into a finite-state transducer that is able to map inflected forms of previously unseen words into lemmas and corresponding morphosyntactic descriptions. We evaluate the system when provided with inflection tables for several languages collected from the Wiktionary.

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Morphological Analysis of Sahidic Coptic for Automatic Glossing
Daniel Smith | Mans Hulden

We report on the implementation of a morphological analyzer for the Sahidic dialect of Coptic, a now extinct Afro-Asiatic language. The system is developed in the finite-state paradigm. The main purpose of the project is provide a method by which scholars and linguists can semi-automatically gloss extant texts written in Sahidic. Since a complete lexicon containing all attested forms in different manuscripts requires significant expertise in Coptic spanning almost 1,000 years, we have equipped the analyzer with a core lexicon and extended it with a “guesser” ability to capture out-of-vocabulary items in any inflection. We also suggest an ASCII transliteration for the language. A brief evaluation is provided.

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The on-line version of Grammatical Dictionary of Polish
Marcin Woliński | Witold Kieraś

We present the new online edition of a dictionary of Polish inflection ― the Grammatical Dictionary of Polish (http://sgjp.pl). The dictionary is interesting for several reasons: it is comprehensive (over 330,000 lexemes corresponding to almost 4,300,000 different textual words; 1116 handcrafted inflectional patterns), the inflection is presented in an explicit manner in the form of carefully designed tables, the user interface facilitates advanced queries by several features (lemmas, forms, applicable grammatical categories, types of inflection). Moreover, the data of the dictionary is used in morphological analysers, including our product Morfeusz (http://sgjp.pl/morfeusz). From the start, the dictionary was meant to be comfortable for the human reader as well as to be ready for use in NLP applications. In the paper we briefly discuss both aspects of the resource.

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Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
Maximilian Köper | Sabine Schulte im Walde

This paper presents a collection of 350,000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a numerical rating of a word. We applied this algorithm to abstractness, arousal, imageability and valence. Comparison with human ratings reveals high correlation across all rating types. The full resource is publically available at: http://www.ims.uni-stuttgart.de/data/affective_norms/

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Latin Vallex. A Treebank-based Semantic Valency Lexicon for Latin
Marco Passarotti | Berta González Saavedra | Christophe Onambele

Despite a centuries-long tradition in lexicography, Latin lacks state-of-the-art computational lexical resources. This situation is strictly related to the still quite limited amount of linguistically annotated textual data for Latin, which can help the building of new lexical resources by supporting them with empirical evidence. However, projects for creating new language resources for Latin have been launched over the last decade to fill this gap. In this paper, we present Latin Vallex, a valency lexicon for Latin built in mutual connection with the semantic and pragmatic annotation of two Latin treebanks featuring texts of different eras. On the one hand, such a connection between the empirical evidence provided by the treebanks and the lexicon allows to enhance each frame entry in the lexicon with its frequency in real data. On the other hand, each valency-capable word in the treebanks is linked to a frame entry in the lexicon.

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A Framework for Cross-lingual/Node-wise Alignment of Lexical-Semantic Resources
Yoshihiko Hayashi

Given lexical-semantic resources in different languages, it is useful to establish cross-lingual correspondences, preferably with semantic relation labels, between the concept nodes in these resources. This paper presents a framework for enabling a cross-lingual/node-wise alignment of lexical-semantic resources, where cross-lingual correspondence candidates are first discovered and ranked, and then classified by a succeeding module. Indeed, we propose that a two-tier classifier configuration is feasible for the second module: the first classifier filters out possibly irrelevant correspondence candidates and the second classifier assigns a relatively fine-grained semantic relation label to each of the surviving candidates. The results of Japanese-to-English alignment experiments using EDR Electronic Dictionary and Princeton WordNet are described to exemplify the validity of the proposal.

The last two decades have seen the development of various semantic lexical resources such as WordNet (Miller, 1995) and the USAS semantic lexicon (Rayson et al., 2004), which have played an important role in the areas of natural language processing and corpus-based studies. Recently, increasing efforts have been devoted to extending the semantic frameworks of existing lexical knowledge resources to cover more languages, such as EuroWordNet and Global WordNet. In this paper, we report on the construction of large-scale multilingual semantic lexicons for twelve languages, which employ the unified Lancaster semantic taxonomy and provide a multilingual lexical knowledge base for the automatic UCREL semantic annotation system (USAS). Our work contributes towards the goal of constructing larger-scale and higher-quality multilingual semantic lexical resources and developing corpus annotation tools based on them. Lexical coverage is an important factor concerning the quality of the lexicons and the performance of the corpus annotation tools, and in this experiment we focus on evaluating the lexical coverage achieved by the multilingual lexicons and semantic annotation tools based on them. Our evaluation shows that some semantic lexicons such as those for Finnish and Italian have achieved lexical coverage of over 90% while others need further expansion.

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Building Concept Graphs from Monolingual Dictionary Entries
Gábor Recski

We present the dict_to_4lang tool for processing entries of three monolingual dictionaries of English and mapping definitions to concept graphs following the 4lang principles of semantic representation introduced by (Kornai, 2010). 4lang representations are domain- and language-independent, and make use of only a very limited set of primitives to encode the meaning of all utterances. Our pipeline relies on the Stanford Dependency Parser for syntactic analysis, the dep to 4lang module then builds directed graphs of concepts based on dependency relations between words in each definition. Several issues are handled by construction-specific rules that are applied to the output of dep_to_4lang. Manual evaluation suggests that ca. 75% of graphs built from the Longman Dictionary are either entirely correct or contain only minor errors. dict_to_4lang is available under an MIT license as part of the 4lang library and has been used successfully in measuring Semantic Textual Similarity (Recski and Ács, 2015). An interactive demo of core 4lang functionalities is available at http://4lang.hlt.bme.hu.

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Semantic Layer of the Valence Dictionary of Polish Walenty
Elżbieta Hajnicz | Anna Andrzejczuk | Tomasz Bartosiak

This article presents the semantic layer of Walenty―a new valence dictionary of Polish predicates, with a number of novel features, as compared to other such dictionaries. The dictionary contains two layers, syntactic and semantic. The syntactic layer describes syntactic and morphosyntactic constraints predicates put on their dependants. In particular, it includes a comprehensive and powerful phraseological component. The semantic layer shows how predicates and their arguments are involved in a described situation in an utterance. These two layers are connected, representing how semantic arguments can be realised on the surface. Each syntactic schema and each semantic frame are illustrated by at least one exemplary sentence attested in linguistic reality. The semantic layer consists of semantic frames represented as lists of pairs and connected with PlWordNet lexical units. Semantic roles have a two-level representation (basic roles are provided with an attribute) enabling representation of arguments in a flexible way. Selectional preferences are based on PlWordNet structure as well.

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Italian VerbNet: A Construction-based Approach to Italian Verb Classification
Lucia Busso | Alessandro Lenci

This paper proposes a new method for Italian verb classification -and a preliminary example of resulting classes- inspired by Levin (1993) and VerbNet (Kipper-Schuler, 2005), yet partially independent from these resources; we achieved such a result by integrating Levin and VerbNet’s models of classification with other theoretic frameworks and resources. The classification is rooted in the constructionist framework (Goldberg, 1995; 2006) and is distribution-based. It is also semantically characterized by a link to FrameNet’ssemanticframesto represent the event expressed by a class. However, the new Italian classes maintain the hierarchic “tree” structure and monotonic nature of VerbNet’s classes, and, where possible, the original names (e.g.: Verbs of Killing, Verbs of Putting, etc.). We therefore propose here a taxonomy compatible with VerbNet but at the same time adapted to Italian syntax and semantics. It also addresses a number of problems intrinsic to the original classifications, such as the role of argument alternations, here regarded simply as epiphenomena, consistently with the constructionist approach.

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A Large Rated Lexicon with French Medical Words
Natalia Grabar | Thierry Hamon

Patients are often exposed to medical terms, such as anosognosia, myelodysplastic, or hepatojejunostomy, that can be semantically complex and hardly understandable by non-experts in medicine. Hence, it is important to assess which words are potentially non-understandable and require further explanations. The purpose of our work is to build specific lexicon in which the words are rated according to whether they are understandable or non-understandable. We propose to work with medical words in French such as provided by an international medical terminology. The terms are segmented in single words and then each word is manually processed by three annotators. The objective is to assign each word into one of the three categories: I can understand, I am not sure, I cannot understand. The annotators do not have medical training nor they present specific medical problems. They are supposed to represent an average patient. The inter-annotator agreement is then computed. The content of the categories is analyzed. Possible applications in which this lexicon can be helpful are proposed and discussed. The rated lexicon is freely available for the research purposes. It is accessible online at http://natalia.grabar.perso.sfr.fr/rated-lexicon.html

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Best of Both Worlds: Making Word Sense Embeddings Interpretable
Alexander Panchenko

Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.

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VerbLexPor: a lexical resource with semantic roles for Portuguese
Leonardo Zilio | Maria José Bocorny Finatto | Aline Villavicencio

This paper presents a lexical resource developed for Portuguese. The resource contains sentences annotated with semantic roles. The sentences were extracted from two domains: Cardiology research papers and newspaper articles. Both corpora were analyzed with the PALAVRAS parser and subsequently processed with a subcategorization frames extractor, so that each sentence that contained at least one main verb was stored in a database together with its syntactic organization. The annotation was manually carried out by a linguist using an annotation interface. Both the annotated and non-annotated data were exported to an XML format, which is readily available for download. The reason behind exporting non-annotated data is that there is syntactic information collected from the parser annotation in the non-annotated data, and this could be useful for other researchers. The sentences from both corpora were annotated separately, so that it is possible to access sentences either from the Cardiology or from the newspaper corpus. The full resource presents more than seven thousand semantically annotated sentences, containing 192 different verbs and more than 15 thousand individual arguments and adjuncts.

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A Multilingual Predicate Matrix
Maddalen Lopez de Lacalle | Egoitz Laparra | Itziar Aldabe | German Rigau

This paper presents the Predicate Matrix 1.3, a lexical resource resulting from the integration of multiple sources of predicate information including FrameNet, VerbNet, PropBank and WordNet. This new version of the Predicate Matrix has been extended to cover nominal predicates by adding mappings to NomBank. Similarly, we have integrated resources in Spanish, Catalan and Basque. As a result, the Predicate Matrix 1.3 provides a multilingual lexicon to allow interoperable semantic analysis in multiple languages.

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A Gold Standard for Scalar Adjectives
Bryan Wilkinson | Oates Tim

We present a gold standard for evaluating scale membership and the order of scalar adjectives. In addition to evaluating existing methods of ordering adjectives, this knowledge will aid in studying the organization of adjectives in the lexicon. This resource is the result of two elicitation tasks conducted with informants from Amazon Mechanical Turk. The first task is notable for gathering open-ended lexical data from informants. The data is analyzed using Cultural Consensus Theory, a framework from anthropology, to not only determine scale membership but also the level of consensus among the informants (Romney et al., 1986). The second task gathers a culturally salient ordering of the words determined to be members. We use this method to produce 12 scales of adjectives for use in evaluation.

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VerbCROcean: A Repository of Fine-Grained Semantic Verb Relations for Croatian
Ivan Sekulić | Jan Šnajder

In this paper we describe VerbCROcean, a broad-coverage repository of fine-grained semantic relations between Croatian verbs. Adopting the methodology of Chklovski and Pantel (2004) used for acquiring the English VerbOcean, we first acquire semantically related verb pairs from a web corpus hrWaC by relying on distributional similarity of subject-verb-object paths in the dependency trees. We then classify the semantic relations between each pair of verbs as similarity, intensity, antonymy, or happens-before, using a number of manually-constructed lexico-syntatic patterns. We evaluate the quality of the resulting resource on a manually annotated sample of 1000 semantic verb relations. The evaluation revealed that the predictions are most accurate for the similarity relation, and least accurate for the intensity relation. We make available two variants of VerbCROcean: a coverage-oriented version, containing about 36k verb pairs at a precision of 41%, and a precision-oriented version containing about 5k verb pairs, at a precision of 56%.

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Enriching a Portuguese WordNet using Synonyms from a Monolingual Dictionary
Alberto Simões | Xavier Gómez Guinovart | José João Almeida

In this article we present an exploratory approach to enrich a WordNet-like lexical ontology with the synonyms present in a standard monolingual Portuguese dictionary. The dictionary was converted from PDF into XML and senses were automatically identified and annotated. This allowed us to extract them, independently of definitions, and to create sets of synonyms (synsets). These synsets were then aligned with WordNet synsets, both in the same language (Portuguese) and projecting the Portuguese terms into English, Spanish and Galician. This process allowed both the addition of new term variants to existing synsets, as to create new synsets for Portuguese.

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Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset
Vuk Batanović | Boško Nikolić | Milan Milosavljević

Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.

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ANTUSD: A Large Chinese Sentiment Dictionary
Shih-Ming Wang | Lun-Wei Ku

This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/

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Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation
Md Shad Akhtar | Asif Ekbal | Pushpak Bhattacharyya

Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers. A number of benchmark datasets for a wide range of domains have been made available for sentiment analysis, especially in resource-rich languages. In this paper we assess the challenges of SA in Hindi by providing a benchmark setup, where we create an annotated dataset of high quality, build machine learning models for sentiment analysis in order to show the effective usage of the dataset, and finally make the resource available to the community for further advancement of research. The dataset comprises of Hindi product reviews crawled from various online sources. Each sentence of the review is annotated with aspect term and its associated sentiment. As classification algorithms we use Conditional Random Filed (CRF) and Support Vector Machine (SVM) for aspect term extraction and sentiment analysis, respectively. Evaluation results show the average F-measure of 41.07% for aspect term extraction and accuracy of 54.05% for sentiment classification.

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Gulf Arabic Linguistic Resource Building for Sentiment Analysis
Wafia Adouane | Richard Johansson

This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this gap, we created these two main linguistic resources. Then we conducted different experiments: use Naive Bayes classifier without any lexicon; add a sentiment lexicon designed basically for MSA; use only the compiled Gulf Arabic sentiment lexicon and finally use both MSA and Gulf Arabic sentiment lexicons. The Gulf Arabic lexicon gives a good improvement of the classifier accuracy (90.54 %) over a baseline that does not use the lexicon (82.81%), while the MSA lexicon causes the accuracy to drop to (76.83%). Moreover, mixing MSA and Gulf Arabic lexicons causes the accuracy to drop to (84.94%) compared to using only Gulf Arabic lexicon. This indicates that it is useless to use MSA resources to deal with Gulf Arabic due to the considerable differences and conflicting structures between these two languages.

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Using Data Mining Techniques for Sentiment Shifter Identification
Samira Noferesti | Mehrnoush Shamsfard

Sentiment shifters, i.e., words and expressions that can affect text polarity, play an important role in opinion mining. However, the limited ability of current automated opinion mining systems to handle shifters represents a major challenge. The majority of existing approaches rely on a manual list of shifters; few attempts have been made to automatically identify shifters in text. Most of them just focus on negating shifters. This paper presents a novel and efficient semi-automatic method for identifying sentiment shifters in drug reviews, aiming at improving the overall accuracy of opinion mining systems. To this end, we use weighted association rule mining (WARM), a well-known data mining technique, for finding frequent dependency patterns representing sentiment shifters from a domain-specific corpus. These patterns that include different kinds of shifter words such as shifter verbs and quantifiers are able to handle both local and long-distance shifters. We also combine these patterns with a lexicon-based approach for the polarity classification task. Experiments on drug reviews demonstrate that extracted shifters can improve the precision of the lexicon-based approach for polarity classification 9.25 percent.

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Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus
Nicholas Asher | Julie Hunter | Mathieu Morey | Benamara Farah | Stergos Afantenos

This paper describes the STAC resource, a corpus of multi-party chats annotated for discourse structure in the style of SDRT (Asher and Lascarides, 2003; Lascarides and Asher, 2009). The main goal of the STAC project is to study the discourse structure of multi-party dialogues in order to understand the linguistic strategies adopted by interlocutors to achieve their conversational goals, especially when these goals are opposed. The STAC corpus is not only a rich source of data on strategic conversation, but also the first corpus that we are aware of that provides full discourse structures for multi-party dialogues. It has other remarkable features that make it an interesting resource for other topics: interleaved threads, creative language, and interactions between linguistic and extra-linguistic contexts.

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Purely Corpus-based Automatic Conversation Authoring
Guillaume Dubuisson Duplessis | Vincent Letard | Anne-Laure Ligozat | Sophie Rosset

This paper presents an automatic corpus-based process to author an open-domain conversational strategy usable both in chatterbot systems and as a fallback strategy for out-of-domain human utterances. Our approach is implemented on a corpus of television drama subtitles. This system is used as a chatterbot system to collect a corpus of 41 open-domain textual dialogues with 27 human participants. The general capabilities of the system are studied through objective measures and subjective self-reports in terms of understandability, repetition and coherence of the system responses selected in reaction to human utterances. Subjective evaluations of the collected dialogues are presented with respect to amusement, engagement and enjoyability. The main factors influencing those dimensions in our chatterbot experiment are discussed.

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Dialogue System Characterisation by Back-channelling Patterns Extracted from Dialogue Corpus
Masashi Inoue | Hiroshi Ueno

In this study, we describe the use of back-channelling patterns extracted from a dialogue corpus as a mean to characterising text-based dialogue systems. Our goal was to provide system users with the feeling that they are interacting with distinct individuals rather than artificially created characters. An analysis of the corpus revealed that substantial difference exists among speakers regarding the usage patterns of back-channelling. The patterns consist of back-channelling frequency, types, and expressions. They were used for system characterisation. Implemented system characters were tested by asking users of the dialogue system to identify the source speakers in the corpus. Experimental results suggest that possibility of using back-channelling patterns alone to characterize the dialogue system in some cases even among the same age and gender groups.

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Towards Automatic Identification of Effective Clues for Team Word-Guessing Games
Eli Pincus | David Traum

Team word-guessing games where one player, the clue-giver, gives clues attempting to elicit a target-word from another player, the receiver, are a popular form of entertainment and also used for educational purposes. Creating an engaging computational agent capable of emulating a talented human clue-giver in a timed word-guessing game depends on the ability to provide effective clues (clues able to elicit a correct guess from a human receiver). There are many available web resources and databases that can be mined for the raw material for clues for target-words; however, a large number of those clues are unlikely to be able to elicit a correct guess from a human guesser. In this paper, we propose a method for automatically filtering a clue corpus for effective clues for an arbitrary target-word from a larger set of potential clues, using machine learning on a set of features of the clues, including point-wise mutual information between a clue’s constituent words and a clue’s target-word. The results of the experiments significantly improve the average clue quality over previous approaches, and bring quality rates in-line with measures of human clue quality derived from a corpus of human-human interactions. The paper also introduces the data used to develop this method; audio recordings of people making guesses after having heard the clues being spoken by a synthesized voice.

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Automatic Construction of Discourse Corpora for Dialogue Translation
Longyue Wang | Xiaojun Zhang | Zhaopeng Tu | Andy Way | Qun Liu

In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.

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Using Contextual Information for Machine Translation Evaluation
Marina Fomicheva | Núria Bel

Automatic evaluation of Machine Translation (MT) is typically approached by measuring similarity between the candidate MT and a human reference translation. An important limitation of existing evaluation systems is that they are unable to distinguish candidate-reference differences that arise due to acceptable linguistic variation from the differences induced by MT errors. In this paper we present a new metric, UPF-Cobalt, that addresses this issue by taking into consideration the syntactic contexts of candidate and reference words. The metric applies a penalty when the words are similar but the contexts in which they occur are not equivalent. In this way, Machine Translations (MTs) that are different from the human translation but still essentially correct are distinguished from those that share high number of words with the reference but alter the meaning of the sentence due to translation errors. The results show that the method proposed is indeed beneficial for automatic MT evaluation. We report experiments based on two different evaluation tasks with various types of manual quality assessment. The metric significantly outperforms state-of-the-art evaluation systems in varying evaluation settings.

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Bootstrapping a Hybrid MT System to a New Language Pair
João António Rodrigues | Nuno Rendeiro | Andreia Querido | Sanja Štajner | António Branco

The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system.

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Filtering Wiktionary Triangles by Linear Mbetween Distributed Word Models
Márton Makrai

Word translations arise in dictionary-like organization as well as via machine learning from corpora. The former is exemplified by Wiktionary, a crowd-sourced dictionary with editions in many languages. Ács et al. (2013) obtain word translations from Wiktionary with the pivot-based method, also called triangulation, that infers word translations in a pair of languages based on translations to other, typically better resourced ones called pivots. Triangulation may introduce noise if words in the pivot are polysemous. The reliability of each triangulated translation is basically estimated by the number of pivot languages (Tanaka et al 1994). Mikolov et al (2013) introduce a method for generating or scoring word translations. Translation is formalized as a linear mapping between distributed vector space models (VSM) of the two languages. VSMs are trained on monolingual data, while the mapping is learned in a supervised fashion, using a seed dictionary of some thousand word pairs. The mapping can be used to associate existing translations with a real-valued similarity score. This paper exploits human labor in Wiktionary combined with distributional information in VSMs. We train VSMs on gigaword corpora, and the linear translation mapping on direct (non-triangulated) Wiktionary pairs. This mapping is used to filter triangulated translations based on scores. The motivation is that scores by the mapping may be a smoother measure of merit than considering only the number of pivot for the triangle. We evaluate the scores against dictionaries extracted from parallel corpora (Tiedemann 2012). We show that linear translation really provides a more reliable method for triangle scoring than pivot count. The methods we use are language-independent, and the training data is easy to obtain for many languages. We chose the German-Hungarian pair for evaluation, in which the filtered triangles resulting from our experiments are the greatest freely available list of word translations we are aware of.

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Translation Errors and Incomprehensibility: a Case Study using Machine-Translated Second Language Proficiency Tests
Takuya Matsuzaki | Akira Fujita | Naoya Todo | Noriko H. Arai

This paper reports on an experiment where 795 human participants answered to the questions taken from second language proficiency tests that were translated to their native language. The output of three machine translation systems and two different human translations were used as the test material. We classified the translation errors in the questions according to an error taxonomy and analyzed the participants’ response on the basis of the type and frequency of the translation errors. Through the analysis, we identified several types of errors that deteriorated most the accuracy of the participants’ answers, their confidence on the answers, and their overall evaluation of the translation quality.

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Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models
Steven Neale | Luís Gomes | Eneko Agirre | Oier Lopez de Lacalle | António Branco

Although it is commonly assumed that word sense disambiguation (WSD) should help to improve lexical choice and improve the quality of machine translation systems, how to successfully integrate word senses into such systems remains an unanswered question. Some successful approaches have involved reformulating either WSD or the word senses it produces, but work on using traditional word senses to improve machine translation have met with limited success. In this paper, we build upon previous work that experimented on including word senses as contextual features in maxent-based translation models. Training on a large, open-domain corpus (Europarl), we demonstrate that this aproach yields significant improvements in machine translation from English to Portuguese.

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SuperCAT: The (New and Improved) Corpus Analysis Toolkit
K. Bretonnel Cohen | William A. Baumgartner Jr. | Irina Temnikova

This paper reports SuperCAT, a corpus analysis toolkit. It is a radical extension of SubCAT, the Sublanguage Corpus Analysis Toolkit, from sublanguage analysis to corpus analysis in general. The idea behind SuperCAT is that representative corpora have no tendency towards closure―that is, they tend towards infinity. In contrast, non-representative corpora have a tendency towards closure―roughly, finiteness. SuperCAT focuses on general techniques for the quantitative description of the characteristics of any corpus (or other language sample), particularly concerning the characteristics of lexical distributions. Additionally, SuperCAT features a complete re-engineering of the previous SubCAT architecture.

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LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl
Szymon Roziewski | Wojciech Stokowiec

The web data contains immense amount of data, hundreds of billion words are waiting to be extracted and used for language research. In this work we introduce our tool LanguageCrawl which allows NLP researchers to easily construct web-scale corpus from Common Crawl Archive: a petabyte scale, open repository of web crawl information. Three use-cases are presented: filtering Polish websites, building an N-gram corpora and training continuous skip-gram language model with hierarchical softmax. Each of them has been implemented within the LanguageCrawl toolkit, with the possibility to adjust specified language and N-gram ranks. Special effort has been put on high computing efficiency, by applying highly concurrent multitasking. We make our tool publicly available to enrich NLP resources. We strongly believe that our work will help to facilitate NLP research, especially in under-resourced languages, where the lack of appropriately sized corpora is a serious hindrance to applying data-intensive methods, such as deep neural networks.

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Features for Generic Corpus Querying
Thomas Eckart | Christoph Kuras | Uwe Quasthoff

The availability of large corpora for more and more languages enforces generic querying and standard interfaces. This development is especially relevant in the context of integrated research environments like CLARIN or DARIAH. The paper focuses on several applications and implementation details on the basis of a unified corpus format, a unique POS tag set, and prepared data for word similarities. All described data or applications are already or will be in the near future accessible via well-documented RESTful Web services. The target group are all kinds of interested persons with varying level of experience in programming or corpus query languages.

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European Union Language Resources in Sketch Engine
Vít Baisa | Jan Michelfeit | Marek Medveď | Miloš Jakubíček

Several parallel corpora built from European Union language resources are presented here. They were processed by state-of-the-art tools and made available for researchers in the corpus manager Sketch Engine. A completely new resource is introduced: EUR-Lex Corpus, being one of the largest parallel corpus available at the moment, containing 840 million English tokens and the largest language pair English-French has more than 25 million aligned segments (paragraphs).

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Corpus Query Lingua Franca (CQLF)
Piotr Bański | Elena Frick | Andreas Witt

The present paper describes Corpus Query Lingua Franca (ISO CQLF), a specification designed at ISO Technical Committee 37 Subcommittee 4 “Language resource management” for the purpose of facilitating the comparison of properties of corpus query languages. We overview the motivation for this endeavour and present its aims and its general architecture. CQLF is intended as a multi-part specification; here, we concentrate on the basic metamodel that provides a frame that the other parts fit in.

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A sense-based lexicon of count and mass expressions: The Bochum English Countability Lexicon
Tibor Kiss | Francis Jeffry Pelletier | Halima Husic | Roman Nino Simunic | Johanna Marie Poppek

The present paper describes the current release of the Bochum English Countability Lexicon (BECL 2.1), a large empirical database consisting of lemmata from Open ANC (http://www.anc.org) with added senses from WordNet (Fellbaum 1998). BECL 2.1 contains ≈ 11,800 annotated noun-sense pairs, divided in four major countability classes and 18 fine-grained subclasses. In the current version, BECL also provides information on nouns whose senses occur in more than one class allowing a closer look on polysemy and homonymy with regard to countability. Further included are sets of similar senses using the Leacock and Chodorow (LCH) score for semantic similarity (Leacock & Chodorow 1998), information on orthographic variation, on the completeness of all WordNet senses in the database and an annotated representation of different types of proper names. The further development of BECL will investigate the different countability classes of proper names and the general relation between semantic similarity and countability as well as recurring syntactic patterns for noun-sense pairs. The BECL 2.1 database is also publicly available via http://count-and-mass.org.

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Detecting Optional Arguments of Verbs
András Kornai | Dávid Márk Nemeskey | Gábor Recski

We propose a novel method for detecting optional arguments of Hungarian verbs using only positive data. We introduce a custom variant of collexeme analysis that explicitly models the noise in verb frames. Our method is, for the most part, unsupervised: we use the spectral clustering algorithm described in Brew and Schulte in Walde (2002) to build a noise model from a short, manually verified seed list of verbs. We experimented with both raw count- and context-based clusterings and found their performance almost identical. The code for our algorithm and the frame list are freely available at http://hlt.bme.hu/en/resources/tade.

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Leveraging Native Data to Correct Preposition Errors in Learners’ Dutch
Lennart Kloppenburg | Malvina Nissim

We address the task of automatically correcting preposition errors in learners’ Dutch by modelling preposition usage in native language. Specifically, we build two models exploiting a large corpus of Dutch. The first is a binary model for detecting whether a preposition should be used at all in a given position or not. The second is a multiclass model for selecting the appropriate preposition in case one should be used. The models are tested on native as well as learners data. For the latter we exploit a crowdsourcing strategy to elicit native judgements. On native test data the models perform very well, showing that we can model preposition usage appropriately. However, the evaluation on learners’ data shows that while detecting that a given preposition is wrong is doable reasonably well, detecting the absence of a preposition is a lot more difficult. Observing such results and the data we deal with, we envisage various ways of improving performance, and report them in the final section of this article.

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D(H)ante: A New Set of Tools for XIII Century Italian
Angelo Basile | Federico Sangati

In this paper we describe 1) the process of converting a corpus of Dante Alighieri from a TEI XML format in to a pseudo-CoNLL format; 2) how a pos-tagger trained on modern Italian performs on Dante’s Italian 3) the performances of two different pos-taggers trained on the given corpus. We are making our conversion scripts and models available to the community. The two other models trained on the corpus performs reasonably well. The tool used for the conversion process might turn useful for bridging the gap between traditional digital humanities and modern NLP applications since the TEI original format is not usually suitable for being processed with standard NLP tools. We believe our work will serve both communities: the DH community will be able to tag new documents and the NLP world will have an easier way in converting existing documents to a standardized machine-readable format.

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Multilevel Annotation of Agreement and Disagreement in Italian News Blogs
Fabio Celli | Giuseppe Riccardi | Firoj Alam

In this paper, we present a corpus of news blog conversations in Italian annotated with gold standard agreement/disagreement relations at message and sentence levels. This is the first resource of this kind in Italian. From the analysis of ADRs at the two levels emerged that agreement annotated at message level is consistent and generally reflected at sentence level, moreover, the argumentation structure of disagreement is more complex than agreement. The manual error analysis revealed that this resource is useful not only for the analysis of argumentation, but also for the detection of irony/sarcasm in online debates. The corpus and annotation tool are available for research purposes on request.

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Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization
Şaziye Betül Özateş | Arzucan Özgür | Dragomir Radev

We introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization. We adapt and investigate the effects of two untyped dependency tree kernels, which have originally been proposed for relation extraction, to the multi-document summarization problem. In addition, we propose a series of novel dependency grammar based kernels to better represent the syntactic and semantic similarities among the sentences. The proposed methods incorporate the type information of the dependency relations for sentence similarity calculation. To our knowledge, this is the first study that investigates using dependency tree based sentence similarity for multi-document summarization.

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Discontinuous Verb Phrases in Parsing and Machine Translation of English and German
Sharid Loáiciga | Kristina Gulordava

In this paper, we focus on the verb-particle (V-Prt) split construction in English and German and its difficulty for parsing and Machine Translation (MT). For German, we use an existing test suite of V-Prt split constructions, while for English, we build a new and comparable test suite from raw data. These two data sets are then used to perform an analysis of errors in dependency parsing, word-level alignment and MT, which arise from the discontinuous order in V-Prt split constructions. In the automatic alignments of parallel corpora, most of the particles align to NULL. These mis-alignments and the inability of phrase-based MT system to recover discontinuous phrases result in low quality translations of V-Prt split constructions both in English and German. However, our results show that the V-Prt split phrases are correctly parsed in 90% of cases, suggesting that syntactic-based MT should perform better on these constructions. We evaluate a syntactic-based MT system on German and compare its performance to the phrase-based system.

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A Lexical Resource for the Identification of “Weak Words” in German Specification Documents
Jennifer Krisch | Melanie Dick | Ronny Jauch | Ulrich Heid

We report on the creation of a lexical resource for the identification of potentially unspecific or imprecise constructions in German requirements documentation from the car manufacturing industry. In requirements engineering, such expressions are called “weak words”: they are not sufficiently precise to ensure an unambiguous interpretation by the contractual partners, who for the definition of their cooperation, typically rely on specification documents (Melchisedech, 2000); an example are dimension adjectives, such as kurz or lang (‘short’, ‘long’) which need to be modified by adverbials indicating the exact duration, size etc. Contrary to standard practice in requirements engineering, where the identification of such weak words is merely based on stopword lists, we identify weak uses in context, by querying annotated text. The queries are part of the resource, as they define the conditions when a word use is weak. We evaluate the recognition of weak uses on our development corpus and on an unseen evaluation corpus, reaching stable F1-scores above 0.95.

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Recent Advances in Development of a Lexicon-Grammar of Polish: PolNet 3.0
Zygmunt Vetulani | Grażyna Vetulani | Bartłomiej Kochanowski

The granularity of PolNet (Polish Wordnet) is the main theoretical issue discussed in the paper. We describe the latest extension of PolNet including valency information of simple verbs and noun-verb collocations using manual and machine-assisted methods. Valency is defined to include both semantic and syntactic selectional restrictions. We assume the valency structure of a verb to be an index of meaning. Consistently we consider it an attribute of a synset. Strict application of this principle results in fine granularity of the verb section of the wordnet. Considering valency as a distinctive feature of synsets was an essential step to transform the initial PolNet (first intended as a lexical ontology) into a lexicon-grammar. For the present refinement of PolNet we assume that the category of language register is a part of meaning. The totality of PolNet 2.0 synsets is being revised in order to split the PolNet 2.0 synsets that contain different register words into register-uniform sub-synsets. We completed this operation for synsets that were used as values of semantic roles. The operation augmented the number of considered synsets by 29%. In the paper we report an extension of the class of collocation-based verb synsets.

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C-WEP―Rich Annotated Collection of Writing Errors by Professionals
Cerstin Mahlow

This paper presents C-WEP, the Collection of Writing Errors by Professionals Writers of German. It currently consists of 245 sentences with grammatical errors. All sentences are taken from published texts. All authors are professional writers with high skill levels with respect to German, the genres, and the topics. The purpose of this collection is to provide seeds for more sophisticated writing support tools as only a very small proportion of those errors can be detected by state-of-the-art checkers. C-WEP is annotated on various levels and freely available.

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Improving corpus search via parsing
Natalia Klyueva | Pavel Straňák

In this paper, we describe an addition to the corpus query system Kontext that enables to enhance the search using syntactic attributes in addition to the existing features, mainly lemmas and morphological categories. We present the enhancements of the corpus query system itself, the attributes we use to represent syntactic structures in data, and some examples of querying the syntactically annotated corpora, such as treebanks in various languages as well as an automatically parsed large corpus.

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Affective Lexicon Creation for the Greek Language
Elisavet Palogiannidi | Polychronis Koutsakis | Elias Iosif | Alexandros Potamianos

Starting from the English affective lexicon ANEW (Bradley and Lang, 1999a) we have created the first Greek affective lexicon. It contains human ratings for the three continuous affective dimensions of valence, arousal and dominance for 1034 words. The Greek affective lexicon is compared with affective lexica in English, Spanish and Portuguese. The lexicon is automatically expanded by selecting a small number of manually annotated words to bootstrap the process of estimating affective ratings of unknown words. We experimented with the parameters of the semantic-affective model in order to investigate their impact to its performance, which reaches 85% binary classification accuracy (positive vs. negative ratings). We share the Greek affective lexicon that consists of 1034 words and the automatically expanded Greek affective lexicon that contains 407K words.

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A Hungarian Sentiment Corpus Manually Annotated at Aspect Level
Martina Katalin Szabó | Veronika Vincze | Katalin Ilona Simkó | Viktor Varga | Viktor Hangya

In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools. The corpus is a unique Hungarian database: to the best of our knowledge, no digitized Hungarian sentiment corpus that is annotated on the level of fragments and targets has been made so far. In addition, many language elements of the corpus, relevant from the point of view of sentiment analysis, got distinct types of tags in the annotation. In this paper, on the one hand, we present the method of annotation, and we discuss the difficulties concerning text annotation process. On the other hand, we provide some quantitative and qualitative data on the corpus. We conclude with a description of the applicability of the corpus.

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Effect Functors for Opinion Inference
Josef Ruppenhofer | Jasper Brandes

Sentiment analysis has so far focused on the detection of explicit opinions. However, of late implicit opinions have received broader attention, the key idea being that the evaluation of an event type by a speaker depends on how the participants in the event are valued and how the event itself affects the participants. We present an annotation scheme for adding relevant information, couched in terms of so-called effect functors, to German lexical items. Our scheme synthesizes and extends previous proposals. We report on an inter-annotator agreement study. We also present results of a crowdsourcing experiment to test the utility of some known and some new functors for opinion inference where, unlike in previous work, subjects are asked to reason from event evaluation to participant evaluation.

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Sentiframes: A Resource for Verb-centered German Sentiment Inference
Manfred Klenner | Michael Amsler

In this paper, a German verb resource for verb-centered sentiment inference is introduced and evaluated. Our model specifies verb polarity frames that capture the polarity effects on the fillers of the verb’s arguments given a sentence with that verb frame. Verb signatures and selectional restrictions are also part of the model. An algorithm to apply the verb resource to treebank sentences and the results of our first evaluation are discussed.

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Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola
Marco Stranisci | Cristina Bosco | Delia Irazú Hernández Farías | Viviana Patti

In this paper we present the TWitterBuonaScuola corpus (TW-BS), a novel Italian linguistic resource for Sentiment Analysis, developed with the main aim of analyzing the online debate on the controversial Italian political reform “Buona Scuola” (Good school), aimed at reorganizing the national educational and training systems. We describe the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtags mainly used by the participants to the debate, while the annotation has been focused on sentiment polarity and irony, but also extended to mark the aspects of the reform that were mainly discussed in the debate. An in-depth study of the disagreement among annotators is included. We describe the collection and annotation stages, and the in-depth analysis of disagreement made with Crowdflower, a crowdsourcing annotation platform.

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NileULex: A Phrase and Word Level Sentiment Lexicon for Egyptian and Modern Standard Arabic
Samhaa R. El-Beltagy

This paper presents NileULex, which is an Arabic sentiment lexicon containing close to six thousands Arabic words and compound phrases. Forty five percent of the terms and expressions in the lexicon are Egyptian or colloquial while fifty five percent are Modern Standard Arabic. While the collection of many of the terms included in the lexicon was done automatically, the actual addition of any term was done manually. One of the important criterions for adding terms to the lexicon, was that they be as unambiguous as possible. The result is a lexicon with a much higher quality than any translated variant or automatically constructed one. To demonstrate that a lexicon such as this can directly impact the task of sentiment analysis, a very basic machine learning based sentiment analyser that uses unigrams, bigrams, and lexicon based features was applied on two different Twitter datasets. The obtained results were compared to a baseline system that only uses unigrams and bigrams. The same lexicon based features were also generated using a publicly available translation of a popular sentiment lexicon. The experiments show that usage of the developed lexicon improves the results over both the baseline and the publicly available lexicon.

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OPFI: A Tool for Opinion Finding in Polish
Aleksander Wawer

The paper contains a description of OPFI: Opinion Finder for the Polish Language, a freely available tool for opinion target extraction. The goal of the tool is opinion finding: a task of identifying tuples composed of sentiment (positive or negative) and its target (about what or whom is the sentiment expressed). OPFI is not dependent on any particular method of sentiment identification and provides a built-in sentiment dictionary as a convenient option. Technically, it contains implementations of three different modes of opinion tuple generation: one hybrid based on dependency parsing and CRF, the second based on shallow parsing and the third on deep learning, namely GRU neural network. The paper also contains a description of related language resources: two annotated treebanks and one set of tweets.

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Rude waiter but mouthwatering pastries! An exploratory study into Dutch Aspect-Based Sentiment Analysis
Orphée De Clercq | Véronique Hoste

The fine-grained task of automatically detecting all sentiment expressions within a given document and the aspects to which they refer is known as aspect-based sentiment analysis. In this paper we present the first full aspect-based sentiment analysis pipeline for Dutch and apply it to customer reviews. To this purpose, we collected reviews from two different domains, i.e. restaurant and smartphone reviews. Both corpora have been manually annotated using newly developed guidelines that comply to standard practices in the field. For our experimental pipeline we perceive aspect-based sentiment analysis as a task consisting of three main subtasks which have to be tackled incrementally: aspect term extraction, aspect category classification and polarity classification. First experiments on our Dutch restaurant corpus reveal that this is indeed a feasible approach that yields promising results.

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Building A Case-based Semantic English-Chinese Parallel Treebank
Huaxing Shi | Tiejun Zhao | Keh-Yih Su

We construct a case-based English-to-Chinese semantic constituent parallel Treebank for a Statistical Machine Translation (SMT) task by labelling each node of the Deep Syntactic Tree (DST) with our refined semantic cases. Since subtree span-crossing is harmful in tree-based SMT, DST is adopted to alleviate this problem. At the same time, we tailor an existing case set to represent bilingual shallow semantic relations more precisely. This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses. Data in our Treebank is from the news domain of Datum corpus. 4,000 sentence pairs are selected to cover various lexicons and part-of-speech (POS) n-gram patterns as much as possible. This paper presents the construction of this case Treebank. Also, we have tested the effect of adopting DST structure in alleviating subtree span-crossing. Our preliminary analysis shows that the compatibility between Chinese and English trees can be significantly increased by transforming the parse-tree into the DST. Furthermore, the human agreement rate in annotation is found to be acceptable (90% in English nodes, 75% in Chinese nodes).

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Uzbek-English and Turkish-English Morpheme Alignment Corpora
Xuansong Li | Jennifer Tracey | Stephen Grimes | Stephanie Strassel

Morphologically-rich languages pose problems for machine translation (MT) systems, including word-alignment errors, data sparsity and multiple affixes. Current alignment models at word-level do not distinguish words and morphemes, thus yielding low-quality alignment and subsequently affecting end translation quality. Models using morpheme-level alignment can reduce the vocabulary size of morphologically-rich languages and overcomes data sparsity. The alignment data based on smallest units reveals subtle language features and enhances translation quality. Recent research proves such morpheme-level alignment (MA) data to be valuable linguistic resources for SMT, particularly for languages with rich morphology. In support of this research trend, the Linguistic Data Consortium (LDC) created Uzbek-English and Turkish-English alignment data which are manually aligned at the morpheme level. This paper describes the creation of MA corpora, including alignment and tagging process and approaches, highlighting annotation challenges and specific features of languages with rich morphology. The light tagging annotation on the alignment layer adds extra value to the MA data, facilitating users in flexibly tailoring the data for various MT model training.

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Parallel Sentence Extraction from Comparable Corpora with Neural Network Features
Chenhui Chu | Raj Dabre | Sadao Kurohashi

Parallel corpora are crucial for machine translation (MT), however they are quite scarce for most language pairs and domains. As comparable corpora are far more available, many studies have been conducted to extract parallel sentences from them for MT. In this paper, we exploit the neural network features acquired from neural MT for parallel sentence extraction. We observe significant improvements for both accuracy in sentence extraction and MT performance.

We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.

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The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine
Mariana Neves | Antonio Jimeno Yepes | Aurélie Névéol

The biomedical scientific literature is a rich source of information not only in the English language, for which it is more abundant, but also in other languages, such as Portuguese, Spanish and French. We present the first freely available parallel corpus of scientific publications for the biomedical domain. Documents from the ”Biological Sciences” and ”Health Sciences” categories were retrieved from the Scielo database and parallel titles and abstracts are available for the following language pairs: Portuguese/English (about 86,000 documents in total), Spanish/English (about 95,000 documents) and French/English (about 2,000 documents). Additionally, monolingual data was also collected for all four languages. Sentences in the parallel corpus were automatically aligned and a manual analysis of 200 documents by native experts found that a minimum of 79% of sentences were correctly aligned in all language pairs. We demonstrate the utility of the corpus by running baseline machine translation experiments. We show that for all language pairs, a statistical machine translation system trained on the parallel corpora achieves performance that rivals or exceeds the state of the art in the biomedical domain. Furthermore, the corpora are currently being used in the biomedical task in the First Conference on Machine Translation (WMT’16).

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Producing Monolingual and Parallel Web Corpora at the Same Time - SpiderLing and Bitextor’s Love Affair
Nikola Ljubešić | Miquel Esplà-Gomis | Antonio Toral | Sergio Ortiz Rojas | Filip Klubička

This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain “.hr” and the Slovene top-level domain “.si”, and extrinsically on the English-Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English-Croatian, English-Finnish, English-Serbian and English-Slovene language pairs.

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Towards Using Social Media to Identify Individuals at Risk for Preventable Chronic Illness
Dane Bell | Daniel Fried | Luwen Huangfu | Mihai Surdeanu | Stephen Kobourov

We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.

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Can Tweets Predict TV Ratings?
Bridget Sommerdijk | Eric Sanders | Antal van den Bosch

We set out to investigate whether TV ratings and mentions of TV programmes on the Twitter social media platform are correlated. If such a correlation exists, Twitter may be used as an alternative source for estimating viewer popularity. Moreover, the Twitter-based rating estimates may be generated during the programme, or even before. We count the occurrences of programme-specific hashtags in an archive of Dutch tweets of eleven popular TV shows broadcast in the Netherlands in one season, and perform correlation tests. Overall we find a strong correlation of 0.82; the correlation remains strong, 0.79, if tweets are counted a half hour before broadcast time. However, the two most popular TV shows account for most of the positive effect; if we leave out the single and second most popular TV shows, the correlation drops to being moderate to weak. Also, within a TV show, correlations between ratings and tweet counts are mostly weak, while correlations between TV ratings of the previous and next shows are strong. In absence of information on previous shows, Twitter-based counts may be a viable alternative to classic estimation methods for TV ratings. Estimates are more reliable with more popular TV shows.

In this paper we consider the problem of out-of-vocabulary term classification in web forum text from the automotive domain. We develop a set of nine domain- and application-specific categories for out-of-vocabulary terms. We then propose a supervised approach to classify out-of-vocabulary terms according to these categories, drawing on features based on word embeddings, and linguistic knowledge of common properties of out-of-vocabulary terms. We show that the features based on word embeddings are particularly informative for this task. The categories that we predict could serve as a preliminary, automatically-generated source of lexical knowledge about out-of-vocabulary terms. Furthermore, we show that this approach can be adapted to give a semi-automated method for identifying out-of-vocabulary terms of a particular category, automotive named entities, that is of particular interest to us.

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Corpus for Customer Purchase Behavior Prediction in Social Media
Shigeyuki Sakaki | Francine Chen | Mandy Korpusik | Yan-Ying Chen

Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. “want a Xperia” as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58% of want user tweets and 35% of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products.

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Segmenting Hashtags using Automatically Created Training Data
Arda Çelebi | Arzucan Özgür

Hashtags, which are commonly composed of multiple words, are increasingly used to convey the actual messages in tweets. Understanding what tweets are saying is getting more dependent on understanding hashtags. Therefore, identifying the individual words that constitute a hashtag is an important, yet a challenging task due to the abrupt nature of the language used in tweets. In this study, we introduce a feature-rich approach based on using supervised machine learning methods to segment hashtags. Our approach is unsupervised in the sense that instead of using manually segmented hashtags for training the machine learning classifiers, we automatically create our training data by using tweets as well as by automatically extracting hashtag segmentations from a large corpus. We achieve promising results with such automatically created noisy training data.

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Exploring Language Variation Across Europe - A Web-based Tool for Computational Sociolinguistics
Dirk Hovy | Anders Johannsen

Language varies not only between countries, but also along regional and socio-demographic lines. This variation is one of the driving factors behind language change. However, investigating language variation is a complex undertaking: the more factors we want to consider, the more data we need. Traditional qualitative methods are not well-suited to do this, an therefore restricted to isolated factors. This reduction limits the potential insights, and risks attributing undue importance to easily observed factors. While there is a large interest in linguistics to increase the quantitative aspect of such studies, it requires training in both variational linguistics and computational methods, a combination that is still not common. We take a first step here to alleviating the problem by providing an interface, www.languagevariation.com, to explore large-scale language variation along multiple socio-demographic factors – without programming knowledge. It makes use of large amounts of data and provides statistical analyses, maps, and interactive features that will enable scholars to explore language variation in a data-driven way.

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Predicting Author Age from Weibo Microblog Posts
Wanru Zhang | Andrew Caines | Dimitrios Alikaniotis | Paula Buttery

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Effects of Sampling on Twitter Trend Detection
Andrew Yates | Alek Kolcz | Nazli Goharian | Ophir Frieder

Much research has focused on detecting trends on Twitter, including health-related trends such as mentions of Influenza-like illnesses or their symptoms. The majority of this research has been conducted using Twitter’s public feed, which includes only about 1% of all public tweets. It is unclear if, when, and how using Twitter’s 1% feed has affected the evaluation of trend detection methods. In this work we use a larger feed to investigate the effects of sampling on Twitter trend detection. We focus on using health-related trends to estimate the prevalence of Influenza-like illnesses based on tweets. We use ground truth obtained from the CDC and Google Flu Trends to explore how the prevalence estimates degrade when moving from a 100% to a 1% sample. We find that using the 1% sample is unlikely to substantially harm ILI estimates made at the national level, but can cause poor performance when estimates are made at the city level.

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Automatic Classification of Tweets for Analyzing Communication Behavior of Museums
Nicolas Foucault | Antoine Courtin

In this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: MuseumWeek. The tweets were automatically classified in four communication categories: sharing experience, promoting participation, interacting with the community, and promoting-informing about the institution. Our classification is multi-class. It combines Support Vector Machines and Naive Bayes methods and is supported by a selection of eighteen subtypes of features of four different kinds: metadata information, punctuation marks, tweet-specific and lexical features. It was tested against a corpus of 1,095 tweets manually annotated by two experts in Natural Language Processing and Information Communication and twelve Community Managers of French museums. We obtained an state-of-the-art result of F1-score of 72% by 10-fold cross-validation. This result is very encouraging since is even better than some state-of-the-art results found in the tweet classification literature.

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Graph-Based Induction of Word Senses in Croatian
Marko Bekavac | Jan Šnajder

Word sense induction (WSI) seeks to induce senses of words from unannotated corpora. In this paper, we address the WSI task for the Croatian language. We adopt the word clustering approach based on co-occurrence graphs, in which senses are taken to correspond to strongly inter-connected components of co-occurring words. We experiment with a number of graph construction techniques and clustering algorithms, and evaluate the sense inventories both as a clustering problem and extrinsically on a word sense disambiguation (WSD) task. In the cluster-based evaluation, Chinese Whispers algorithm outperformed Markov Clustering, yielding a normalized mutual information score of 64.3. In contrast, in WSD evaluation Markov Clustering performed better, yielding an accuracy of about 75%. We are making available two induced sense inventories of 10,000 most frequent Croatian words: one coarse-grained and one fine-grained inventory, both obtained using the Markov Clustering algorithm.

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A Multi-domain Corpus of Swedish Word Sense Annotation
Richard Johansson | Yvonne Adesam | Gerlof Bouma | Karin Hedberg

We describe the word sense annotation layer in Eukalyptus, a freely available five-domain corpus of contemporary Swedish with several annotation layers. The annotation uses the SALDO lexicon to define the sense inventory, and allows word sense annotation of compound segments and multiword units. We give an overview of the new annotation tool developed for this project, and finally present an analysis of the inter-annotator agreement between two annotators.

This work presents parallel corpora automatically annotated with several NLP tools, including lemma and part-of-speech tagging, named-entity recognition and classification, named-entity disambiguation, word-sense disambiguation, and coreference. The corpora comprise both the well-known Europarl corpus and a domain-specific question-answer troubleshooting corpus on the IT domain. English is common in all parallel corpora, with translations in five languages, namely, Basque, Bulgarian, Czech, Portuguese and Spanish. We describe the annotated corpora and the tools used for annotation, as well as annotation statistics for each language. These new resources are freely available and will help research on semantic processing for machine translation and cross-lingual transfer.

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Combining Semantic Annotation of Word Sense & Semantic Roles: A Novel Annotation Scheme for VerbNet Roles on German Language Data
Éva Mújdricza-Maydt | Silvana Hartmann | Iryna Gurevych | Anette Frank

We present a VerbNet-based annotation scheme for semantic roles that we explore in an annotation study on German language data that combines word sense and semantic role annotation. We reannotate a substantial portion of the SALSA corpus with GermaNet senses and a revised scheme of VerbNet roles. We provide a detailed evaluation of the interaction between sense and role annotation. The resulting corpus will allow us to compare VerbNet role annotation for German to FrameNet and PropBank annotation by mapping to existing role annotations on the SALSA corpus. We publish the annotated corpus and detailed guidelines for the new role annotation scheme.

Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60%, 65% and 55% on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc.

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Neural Embedding Language Models in Semantic Clustering of Web Search Results
Andrey Kutuzov | Elizaveta Kuzmenko

In this paper, a new approach towards semantic clustering of the results of ambiguous search queries is presented. We propose using distributed vector representations of words trained with the help of prediction-based neural embedding models to detect senses of search queries and to cluster search engine results page according to these senses. The words from titles and snippets together with semantic relationships between them form a graph, which is further partitioned into components related to different query senses. This approach to search engine results clustering is evaluated against a new manually annotated evaluation data set of Russian search queries. We show that in the task of semantically clustering search results, prediction-based models slightly but stably outperform traditional count-based ones, with the same training corpora.

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

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1 Million Captioned Dutch Newspaper Images
Desmond Elliott | Martijn Kleppe

Images naturally appear alongside text in a wide variety of media, such as books, magazines, newspapers, and in online articles. This type of multi-modal data offers an interesting basis for vision and language research but most existing datasets use crowdsourced text, which removes the images from their original context. In this paper, we introduce the KBK-1M dataset of 1.6 million images in their original context, with co-occurring texts found in Dutch newspapers from 1922 - 1994. The images are digitally scanned photographs, cartoons, sketches, and weather forecasts; the text is generated from OCR scanned blocks. The dataset is suitable for experiments in automatic image captioning, image―article matching, object recognition, and data-to-text generation for weather forecasting. It can also be used by humanities scholars to analyse photographic style changes, the representation of people and societal issues, and new tools for exploring photograph reuse via image-similarity-based search.

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Cross-validating Image Description Datasets and Evaluation Metrics
Josiah Wang | Robert Gaizauskas

The task of automatically generating sentential descriptions of image content has become increasingly popular in recent years, resulting in the development of large-scale image description datasets and the proposal of various metrics for evaluating image description generation systems. However, not much work has been done to analyse and understand both datasets and the metrics. In this paper, we propose using a leave-one-out cross validation (LOOCV) process as a means to analyse multiply annotated, human-authored image description datasets and the various evaluation metrics, i.e. evaluating one image description against other human-authored descriptions of the same image. Such an evaluation process affords various insights into the image description datasets and evaluation metrics, such as the variations of image descriptions within and across datasets and also what the metrics capture. We compute and analyse (i) human upper-bound performance; (ii) ranked correlation between metric pairs across datasets; (iii) lower-bound performance by comparing a set of descriptions describing one image to another sentence not describing that image. Interesting observations are made about the evaluation metrics and image description datasets, and we conclude that such cross-validation methods are extremely useful for assessing and gaining insights into image description datasets and evaluation metrics for image descriptions.

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Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition
Polina Yanovich | Carol Neidle | Dimitris Metaxas

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker.

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Benchmarking Lexical Simplification Systems
Gustavo Paetzold | Lucia Specia

Lexical Simplification is the task of replacing complex words in a text with simpler alternatives. A variety of strategies have been devised for this challenge, yet there has been little effort in comparing their performance. In this contribution, we present a benchmarking of several Lexical Simplification systems. By combining resources created in previous work with automatic spelling and inflection correction techniques, we introduce BenchLS: a new evaluation dataset for the task. Using BenchLS, we evaluate the performance of solutions for various steps in the typical Lexical Simplification pipeline, both individually and jointly. This is the first time Lexical Simplification systems are compared in such fashion on the same data, and the findings introduce many contributions to the field, revealing several interesting properties of the systems evaluated.

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A Multi-Layered Annotated Corpus of Scientific Papers
Beatriz Fisas | Francesco Ronzano | Horacio Saggion

Scientific literature records the research process with a standardized structure and provides the clues to track the progress in a scientific field. Understanding its internal structure and content is of paramount importance for natural language processing (NLP) technologies. To meet this requirement, we have developed a multi-layered annotated corpus of scientific papers in the domain of Computer Graphics. Sentences are annotated with respect to their role in the argumentative structure of the discourse. The purpose of each citation is specified. Special features of the scientific discourse such as advantages and disadvantages are identified. In addition, a grade is allocated to each sentence according to its relevance for being included in a summary. To the best of our knowledge, this complex, multi-layered collection of annotations and metadata characterizing a set of research papers had never been grouped together before in one corpus and therefore constitutes a newer, richer resource with respect to those currently available in the field.

In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e.g. summarization of news articles by news aggregators). We show that, evaluated using ROUGE, numerous algorithms from the literature fail to beat a simple lead-based baseline for this task. However, a supervised approach with lightweight and efficient features improves over the lead-based baseline. Additional human evaluation demonstrates that the supervised approach also performs competitively with a commercial system that uses more sophisticated features.

Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments what users really want? Do they support users in performing the sorts of tasks they are likely to want to perform with reader comments? In this paper we address these questions by doing three things. First, we offer a specification of one possible summary type for reader comment, based on an analysis of reader comment in terms of issues and viewpoints. Second, we define a task-based evaluation framework for reader comment summarization that allows summarization systems to be assessed in terms of how well they support users in a time-limited task of identifying issues and characterising opinion on issues in comments. Third, we describe a pilot evaluation in which we used the task-based evaluation framework to evaluate a prototype reader comment clustering and summarization system, demonstrating the viability of the evaluation framework and illustrating the sorts of insight such an evaluation affords.

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A Novel Evaluation Method for Morphological Segmentation
Javad Nouri | Roman Yangarber

Unsupervised learning of morphological segmentation of words in a language, based only on a large corpus of words, is a challenging task. Evaluation of the learned segmentations is a challenge in itself, due to the inherent ambiguity of the segmentation task. There is no way to posit unique “correct” segmentation for a set of data in an objective way. Two models may arrive at different ways of segmenting the data, which may nonetheless both be valid. Several evaluation methods have been proposed to date, but they do not insist on consistency of the evaluated model. We introduce a new evaluation methodology, which enforces correctness of segmentation boundaries while also assuring consistency of segmentation decisions across the corpus.

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Bilingual Lexicon Extraction at the Morpheme Level Using Distributional Analysis
Amir Hazem | Béatrice Daille

Bilingual lexicon extraction from comparable corpora is usually based on distributional methods when dealing with single word terms (SWT). These methods often treat SWT as single tokens without considering their compositional property. However, many SWT are compositional (composed of roots and affixes) and this information, if taken into account can be very useful to match translational pairs, especially for infrequent terms where distributional methods often fail. For instance, the English compound xenograft which is composed of the root xeno and the lexeme graft can be translated into French compositionally by aligning each of its elements (xeno with xéno and graft with greffe) resulting in the translation: xénogreffe. In this paper, we experiment several distributional modellings at the morpheme level that we apply to perform compositional translation to a subset of French and English compounds. We show promising results using distributional analysis at the root and affix levels. We also show that the adapted approach significantly improve bilingual lexicon extraction from comparable corpora compared to the approach at the word level.

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Remote Elicitation of Inflectional Paradigms to Seed Morphological Analysis in Low-Resource Languages
John Sylak-Glassman | Christo Kirov | David Yarowsky

Structured, complete inflectional paradigm data exists for very few of the world’s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific paradigm elicitation templates. Templates are constructed by linguists using grammatical reference materials to ensure completeness. Each cell in a template is associated with contextual prompts designed to help informants with varying levels of linguistic expertise (from professional translators to untrained native speakers) provide the desired inflected form. To facilitate downstream use in interoperable NLP/HLT applications, each cell is also associated with a language-independent machine-readable set of morphological tags from the UniMorph Schema. This data is useful for seeding morphological analysis and generation software, particularly when the data is representative of the range of surface morphological variation in the language. At present, we have obtained 792 lemmas and 25,056 inflected forms from 15 languages.

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Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms
Christo Kirov | John Sylak-Glassman | Roger Que | David Yarowsky

Wiktionary is a large-scale resource for cross-lingual lexical information with great potential utility for machine translation (MT) and many other NLP tasks, especially automatic morphological analysis and generation. However, it is designed primarily for human viewing rather than machine readability, and presents numerous challenges for generalized parsing and extraction due to a lack of standardized formatting and grammatical descriptor definitions. This paper describes a large-scale effort to automatically extract and standardize the data in Wiktionary and make it available for use by the NLP research community. The methodological innovations include a multidimensional table parsing algorithm, a cross-lexeme, token-frequency-based method of separating inflectional form data from grammatical descriptors, the normalization of grammatical descriptors to a unified annotation scheme that accounts for cross-linguistic diversity, and a verification and correction process that exploits within-language, cross-lexeme table format consistency to minimize human effort. The effort described here resulted in the extraction of a uniquely large normalized resource of nearly 1,000,000 inflectional paradigms across 350 languages. Evaluation shows that even though the data is extracted using a language-independent approach, it is comparable in quantity and quality to data extracted using hand-tuned, language-specific approaches.

Users will interact with an individual app on smart devices (e.g., phone, TV, car) to fulfill a specific goal (e.g. find a photographer), but users may also pursue more complex tasks that will span multiple domains and apps (e.g. plan a wedding ceremony). Planning and executing such multi-app tasks are typically managed by users, considering the required global context awareness. To investigate how users arrange domains/apps to fulfill complex tasks in their daily life, we conducted a user study on 14 participants to collect such data from their Android smart phones. This document 1) summarizes the techniques used in the data collection and 2) provides a brief statistical description of the data. This data guilds the future direction for researchers in the fields of conversational agent and personal assistant, etc. This data is available at http://AppDialogue.com.

The paper describes experimental dialogue data collection activities, as well semantically annotated corpus creation undertaken within EU-funded METALOGUE project(www.metalogue.eu). The project aims to develop a dialogue system with flexible dialogue management to enable system’s adaptive, reactive, interactive and proactive dialogue behavior in setting goals, choosing appropriate strategies and monitoring numerous parallel interpretation and management processes. To achieve these goals negotiation (or more precisely multi-issue bargaining) scenario has been considered as the specific setting and application domain. The dialogue corpus forms the basis for the design of task and interaction models of participants negotiation behavior, and subsequently for dialogue system development which would be capable to replace one of the negotiators. The METALOGUE corpus will be released to the community for research purposes.

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The Negochat Corpus of Human-agent Negotiation Dialogues
Vasily Konovalov | Ron Artstein | Oren Melamud | Ido Dagan

Annotated in-domain corpora are crucial to the successful development of dialogue systems of automated agents, and in particular for developing natural language understanding (NLU) components of such systems. Unfortunately, such important resources are scarce. In this work, we introduce an annotated natural language human-agent dialogue corpus in the negotiation domain. The corpus was collected using Amazon Mechanical Turk following the ‘Wizard-Of-Oz’ approach, where a ‘wizard’ human translates the participants’ natural language utterances in real time into a semantic language. Once dialogue collection was completed, utterances were annotated with intent labels by two independent annotators, achieving high inter-annotator agreement. Our initial experiments with an SVM classifier show that automatically inferring such labels from the utterances is far from trivial. We make our corpus publicly available to serve as an aid in the development of dialogue systems for negotiation agents, and suggest that analogous corpora can be created following our methodology and using our available source code. To the best of our knowledge this is the first publicly available negotiation dialogue corpus.

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The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics
Ryuichiro Higashinaka | Kotaro Funakoshi | Yuka Kobayashi | Michimasa Inaba

Dialogue breakdown detection is a promising technique in dialogue systems. To promote the research and development of such a technique, we organized a dialogue breakdown detection challenge where the task is to detect a system’s inappropriate utterances that lead to dialogue breakdowns in chat. This paper describes the design, datasets, and evaluation metrics for the challenge as well as the methods and results of the submitted runs of the participants.

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The DialogBank
Harry Bunt | Volha Petukhova | Andrei Malchanau | Kars Wijnhoven | Alex Fang

This paper presents the DialogBank, a new language resource consisting of dialogues with gold standard annotations according to the ISO 24617-2 standard. Some of these dialogues have been taken from existing corpora and have been re-annotated according to the ISO standard; others have been annotated directly according to the standard. The ISO 24617-2 annotations have been designed according to the ISO principles for semantic annotation, as formulated in ISO 24617-6. The DialogBank makes use of three alternative representation formats, which are shown to be interoperable.

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Coordinating Communication in the Wild: The Artwalk Dialogue Corpus of Pedestrian Navigation and Mobile Referential Communication
Kris Liu | Jean Fox Tree | Marilyn Walker

The Artwalk Corpus is a collection of 48 mobile phone conversations between 24 pairs of friends and 24 pairs of strangers performing a novel, naturalistically-situated referential communication task. This task produced dialogues which, on average, are just under 40 minutes. The task requires the identification of public art while walking around and navigating pedestrian routes in the downtown area of Santa Cruz, California. The task involves a Director on the UCSC campus with access to maps providing verbal instructions to a Follower executing the task. The task provides a setting for real-world situated dialogic language and is designed to: (1) elicit entrainment and coordination of referring expressions between the dialogue participants, (2) examine the effect of friendship on dialogue strategies, and (3) examine how the need to complete the task while negotiating myriad, unanticipated events in the real world ― such as avoiding cars and other pedestrians ― affects linguistic coordination and other dialogue behaviors. Previous work on entrainment and coordinating communication has primarily focused on similar tasks in laboratory settings where there are no interruptions and no need to navigate from one point to another in a complex space. The corpus provides a general resource for studies on how coordinated task-oriented dialogue changes when we move outside the laboratory and into the world. It can also be used for studies of entrainment in dialogue, and the form and style of pedestrian instruction dialogues, as well as the effect of friendship on dialogic behaviors.

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Managing Linguistic and Terminological Variation in a Medical Dialogue System
Leonardo Campillos Llanos | Dhouha Bouamor | Pierre Zweigenbaum | Sophie Rosset

We introduce a dialogue task between a virtual patient and a doctor where the dialogue system, playing the patient part in a simulated consultation, must reconcile a specialized level, to understand what the doctor says, and a lay level, to output realistic patient-language utterances. This increases the challenges in the analysis and generation phases of the dialogue. This paper proposes methods to manage linguistic and terminological variation in that situation and illustrates how they help produce realistic dialogues. Our system makes use of lexical resources for processing synonyms, inflectional and derivational variants, or pronoun/verb agreement. In addition, specialized knowledge is used for processing medical roots and affixes, ontological relations and concept mapping, and for generating lay variants of terms according to the patient’s non-expert discourse. We also report the results of a first evaluation carried out by 11 users interacting with the system. We evaluated the non-contextual analysis module, which supports the Spoken Language Understanding step. The annotation of task domain entities obtained 91.8% of Precision, 82.5% of Recall, 86.9% of F-measure, 19.0% of Slot Error Rate, and 32.9% of Sentence Error Rate.

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A Corpus of Word-Aligned Asked and Anticipated Questions in a Virtual Patient Dialogue System
Ajda Gokcen | Evan Jaffe | Johnsey Erdmann | Michael White | Douglas Danforth

We present a corpus of virtual patient dialogues to which we have added manually annotated gold standard word alignments. Since each question asked by a medical student in the dialogues is mapped to a canonical, anticipated version of the question, the corpus implicitly defines a large set of paraphrase (and non-paraphrase) pairs. We also present a novel process for selecting the most useful data to annotate with word alignments and for ensuring consistent paraphrase status decisions. In support of this process, we have enhanced the earlier Edinburgh alignment tool (Cohn et al., 2008) and revised and extended the Edinburgh guidelines, in particular adding guidance intended to ensure that the word alignments are consistent with the overall paraphrase status decision. The finished corpus and the enhanced alignment tool are made freely available.

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A CUP of CoFee: A large Collection of feedback Utterances Provided with communicative function annotations
Laurent Prévot | Jan Gorisch | Roxane Bertrand

There have been several attempts to annotate communicative functions to utterances of verbal feedback in English previously. Here, we suggest an annotation scheme for verbal and non-verbal feedback utterances in French including the categories base, attitude, previous and visual. The data comprises conversations, maptasks and negotiations from which we extracted ca. 13,000 candidate feedback utterances and gestures. 12 students were recruited for the annotation campaign of ca. 9,500 instances. Each instance was annotated by between 2 and 7 raters. The evaluation of the annotation agreement resulted in an average best-pair kappa of 0.6. While the base category with the values acknowledgement, evaluation, answer, elicit achieve good agreement, this is not the case for the other main categories. The data sets, which also include automatic extractions of lexical, positional and acoustic features, are freely available and will further be used for machine learning classification experiments to analyse the form-function relationship of feedback.

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Palabras: Crowdsourcing Transcriptions of L2 Speech
Eric Sanders | Pepi Burgos | Catia Cucchiarini | Roeland van Hout

We developed a web application for crowdsourcing transcriptions of Dutch words spoken by Spanish L2 learners. In this paper we discuss the design of the application and the influence of metadata and various forms of feedback. Useful data were obtained from 159 participants, with an average of over 20 transcriptions per item, which seems a satisfactory result for this type of research. Informing participants about how many items they still had to complete, and not how many they had already completed, turned to be an incentive to do more items. Assigning participants a score for their performance made it more attractive for them to carry out the transcription task, but this seemed to influence their performance. We discuss possible advantages and disadvantages in connection with the aim of the research and consider possible lessons for designing future experiments.

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The Uppsala Corpus of Student Writings: Corpus Creation, Annotation, and Analysis
Beáta Megyesi | Jesper Näsman | Anne Palmér

The Uppsala Corpus of Student Writings consists of Swedish texts produced as part of a national test of students ranging in age from nine (in year three of primary school) to nineteen (the last year of upper secondary school) who are studying either Swedish or Swedish as a second language. National tests have been collected since 1996. The corpus currently consists of 2,500 texts containing over 1.5 million tokens. Parts of the texts have been annotated on several linguistic levels using existing state-of-the-art natural language processing tools. In order to make the corpus easy to interpret for scholars in the humanities, we chose the CoNLL format instead of an XML-based representation. Since spelling and grammatical errors are common in student writings, the texts are automatically corrected while keeping the original tokens in the corpus. Each token is annotated with part-of-speech and morphological features as well as syntactic structure. The main purpose of the corpus is to facilitate the systematic and quantitative empirical study of the writings of various student groups based on gender, geographic area, age, grade awarded or a combination of these, synchronically or diachronically. The intention is for this to be a monitor corpus, currently under development.

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Corpus for Children’s Writing with Enhanced Output for Specific Spelling Patterns (2nd and 3rd Grade)
Kay Berkling

This paper describes the collection of the H1 Corpus of children’s weekly writing over the course of 3 months in 2nd and 3rd grades, aged 7-11. The texts were collected within the normal classroom setting by the teacher. Texts of children whose parents signed the permission to donate the texts to science were collected and transcribed. The corpus consists of the elicitation techniques, an overview of the data collected and the transcriptions of the texts both with and without spelling errors, aligned on a word by word basis, as well as the scanned in texts. The corpus is available for research via Linguistic Data Consortium (LDC). Researchers are strongly encouraged to make additional annotations and improvements and return it to the public domain via LDC.

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The COPLE2 corpus: a learner corpus for Portuguese
Amália Mendes | Sandra Antunes | Maarten Janssen | Anabela Gonçalves

We present the COPLE2 corpus, a learner corpus of Portuguese that includes written and spoken texts produced by learners of Portuguese as a second or foreign language. The corpus includes at the moment a total of 182,474 tokens and 978 texts, classified according to the CEFR scales. The original handwritten productions are transcribed in TEI compliant XML format and keep record of all the original information, such as reformulations, insertions and corrections made by the teacher, while the recordings are transcribed and aligned with EXMARaLDA. The TEITOK environment enables different views of the same document (XML, student version, corrected version), a CQP-based search interface, the POS, lemmatization and normalization of the tokens, and will soon be used for error annotation in stand-off format. The corpus has already been a source of data for phonological, lexical and syntactic interlanguage studies and will be used for a data-informed selection of language features for each proficiency level.

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French Learners Audio Corpus of German Speech (FLACGS)
Jane Wottawa | Martine Adda-Decker

The French Learners Audio Corpus of German Speech (FLACGS) was created to compare German speech production of German native speakers (GG) and French learners of German (FG) across three speech production tasks of increasing production complexity: repetition, reading and picture description. 40 speakers, 20 GG and 20 FG performed each of the three tasks, which in total leads to approximately 7h of speech. The corpus was manually transcribed and automatically aligned. Analysis that can be performed on this type of corpus are for instance segmental differences in the speech production of L2 learners compared to native speakers. We chose the realization of the velar nasal consonant engma. In spoken French, engma does not appear in a VCV context which leads to production difficulties in FG. With increasing speech production complexity (reading and picture description), engma is realized as engma + plosive by FG in over 50% of the cases. The results of a two way ANOVA with unequal sample sizes on the durations of the different realizations of engma indicate that duration is a reliable factor to distinguish between engma and engma + plosive in FG productions compared to the engma productions in GG in a VCV context. The FLACGS corpus allows to study L2 production and perception.

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Croatian Error-Annotated Corpus of Non-Professional Written Language
Vanja Štefanec | Nikola Ljubešić | Jelena Kuvač Kraljević

In the paper authors present the Croatian corpus of non-professional written language. Consisting of two subcorpora, i.e. the clinical subcorpus, consisting of written texts produced by speakers with various types of language disorders, and the healthy speakers subcorpus, as well as by the levels of its annotation, it offers an opportunity for different lines of research. The authors present the corpus structure, describe the sampling methodology, explain the levels of annotation, and give some very basic statistics. On the basis of data from the corpus, existing language technologies for Croatian are adapted in order to be implemented in a platform facilitating text production to speakers with language disorders. In this respect, several analyses of the corpus data and a basic evaluation of the developed technologies are presented.

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Training & Quality Assessment of an Optical Character Recognition Model for Northern Haida
Isabell Hubert | Antti Arppe | Jordan Lachler | Eddie A. Santos

We are presenting our work on the creation of the first optical character recognition (OCR) model for Northern Haida, also known as Masset or Xaad Kil, a nearly extinct First Nations language spoken in the Haida Gwaii archipelago in British Columbia, Canada. We are addressing the challenges of training an OCR model for a language with an extensive, non-standard Latin character set as follows: (1) We have compared various training approaches and present the results of practical analyses to maximize recognition accuracy and minimize manual labor. An approach using just one or two pages of Source Images directly performed better than the Image Generation approach, and better than models based on three or more pages. Analyses also suggest that a character’s frequency is directly correlated with its recognition accuracy. (2) We present an overview of current OCR accuracy analysis tools available. (3) We have ported the once de-facto standardized OCR accuracy tools to be able to cope with Unicode input. Our work adds to a growing body of research on OCR for particularly challenging character sets, and contributes to creating the largest electronic corpus for this severely endangered language.

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Legacy language atlas data mining: mapping Kru languages
Dafydd Gibbon

An online tool based on dialectometric methods, DistGraph, is applied to a group of Kru languages of Côte d’Ivoire, Liberia and Burkina Faso. The inputs to this resource consist of tables of languages x linguistic features (e.g. phonological, lexical or grammatical), and statistical and graphical outputs are generated which show similarities and differences between the languages in terms of the features as virtual distances. In the present contribution, attention is focussed on the consonant systems of the languages, a traditional starting point for language comparison. The data are harvested from a legacy language data resource based on fieldwork in the 1970s and 1980s, a language atlas of the Kru languages. The method on which the online tool is based extends beyond documentation of individual languages to the documentation of language groups, and supports difference-based prioritisation in education programmes, decisions on language policy and documentation and conservation funding, as well as research on language typology and heritage documentation of history and migration.

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Data Formats and Management Strategies from the Perspective of Language Resource Producers ― Personal Diachronic and Social Synchronic Data Sharing ―
Kazushi Ohya

This is a report of findings from on-going language documentation research based on three consecutive projects from 2008 to 2016. In the light of this research, we propose that (1) we should stand on the side of language resource producers to enhance the research of language processing. We support personal data management in addition to social data sharing. (2) This support leads to adopting simple data formats instead of the multi-link-path data models proposed as international standards up to the present. (3) We should set up a framework for total language resource study that includes not only pivotal data formats such as standard formats, but also the surroundings of data formation to capture a wider range of language activities, e.g. annotation, hesitant language formation, and reference-referent relations. A study of this framework is expected to be a foundation of rebuilding man-machine interface studies in which we seek to observe generative processes of informational symbols in order to establish a high affinity interface in regard to documentation.

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Curation of Dutch Regional Dictionaries
Henk van den Heuvel | Eric Sanders | Nicoline van der Sijs

This paper describes the process of semi-automatically converting dictionaries from paper to structured text (database) and the integration of these into the CLARIN infrastructure in order to make the dictionaries accessible and retrievable for the research community. The case study at hand is that of the curation of 42 fascicles of the Dictionaries of the Brabantic and Limburgian dialects, and 6 fascicles of the Dictionary of dialects in Gelderland.

Poor digital representation of minority languages further prevents their usability on digital media and devices. The Digital Language Diversity Project, a three-year project funded under the Erasmus+ programme, aims at addressing the problem of low digital representation of EU regional and minority languages by giving their speakers the intellectual an practical skills to create, share, and reuse online digital content. Availability of digital content and technical support to use it are essential prerequisites for the development of language-based digital applications, which in turn can boost digital usage of these languages. In this paper we introduce the project, its aims, objectives and current activities for sustaining digital usability of minority languages through adult education.

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Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker
Delyth Prys | Gruffudd Prys | Dewi Bryn Jones

This paper describes the use of a free, on-line language spelling and grammar checking aid as a vehicle for the collection of a significant (31 million words and rising) corpus of text for academic research in the context of less resourced languages where such data in sufficient quantities are often unavailable. It describes two versions of the corpus: the texts as submitted, prior to the correction process, and the texts following the user’s incorporation of any suggested changes. An overview of the corpus’ contents is given and an analysis of use including usage statistics is also provided. Issues surrounding privacy and the anonymization of data are explored as is the data’s potential use for linguistic analysis, lexical research and language modelling. The method used for gathering this corpus is believed to be unique, and is a valuable addition to corpus studies in a minority language.

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ALT Explored: Integrating an Online Dialectometric Tool and an Online Dialect Atlas
Martijn Wieling | Eva Sassolini | Sebastiana Cucurullo | Simonetta Montemagni

In this paper, we illustrate the integration of an online dialectometric tool, Gabmap, together with an online dialect atlas, the Atlante Lessicale Toscano (ALT-Web). By using a newly created url-based interface to Gabmap, ALT-Web is able to take advantage of the sophisticated dialect visualization and exploration options incorporated in Gabmap. For example, distribution maps showing the distribution in the Tuscan dialect area of a specific dialectal form (selected via the ALT-Web website) are easily obtainable. Furthermore, the complete ALT-Web dataset as well as subsets of the data (selected via the ALT-Web website) can be automatically uploaded and explored in Gabmap. By combining these two online applications, macro- and micro-analyses of dialectal data (respectively offered by Gabmap and ALT-Web) are effectively and dynamically combined.

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LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages
Stephanie Strassel | Jennifer Tracey

In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA’s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tagsets, and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.

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A Computational Perspective on the Romanian Dialects
Alina Maria Ciobanu | Liviu P. Dinu

In this paper we conduct an initial study on the dialects of Romanian. We analyze the differences between Romanian and its dialects using the Swadesh list. We analyze the predictive power of the orthographic and phonetic features of the words, building a classification problem for dialect identification.

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The Alaskan Athabascan Grammar Database
Sebastian Nordhoff | Siri Tuttle | Olga Lovick

This paper describes a repository of example sentences in three endangered Athabascan languages: Koyukon, Upper Tanana, Lower Tanana. The repository allows researchers or language teachers to browse the example sentence corpus to either investigate the languages or to prepare teaching materials. The originally heterogeneous text collection was imported into a SOLR store via the POIO bridge. This paper describes the requirements, implementation, advantages and drawbacks of this approach and discusses the potential to apply it for other languages of the Athabascan family or beyond.

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Constraint-Based Bilingual Lexicon Induction for Closely Related Languages
Arbi Haza Nasution | Yohei Murakami | Toru Ishida

The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction becomes a difficult task for low-resource languages. Pivot language and cognate recognition approach have been proven useful to induce bilingual lexicons for such languages. We analyze the features of closely related languages and define a semantic constraint assumption. Based on the assumption, we propose a constraint-based bilingual lexicon induction for closely related languages by extending constraints and translation pair candidates from recent pivot language approach. We further define three constraint sets based on language characteristics. In this paper, two controlled experiments are conducted. The former involves four closely related language pairs with different language pair similarities, and the latter focuses on sense connectivity between non-pivot words and pivot words. We evaluate our result with F-measure. The result indicates that our method works better on voluminous input dictionaries and high similarity languages. Finally, we introduce a strategy to use proper constraint sets for different goals and language characteristics.

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WTF-LOD - A New Resource for Large-Scale NER Evaluation
Lubomir Otrusina | Pavel Smrz

This paper introduces the Web TextFull linkage to Linked Open Data (WTF-LOD) dataset intended for large-scale evaluation of named entity recognition (NER) systems. First, we present the process of collecting data from the largest publically-available textual corpora, including Wikipedia dumps, monthly runs of the CommonCrawl, and ClueWeb09/12. We discuss similarities and differences of related initiatives such as WikiLinks and WikiReverse. Our work primarily focuses on links from “textfull” documents (links surrounded by a text that provides a useful context for entity linking), de-duplication of the data and advanced cleaning procedures. Presented statistics demonstrate that the collected data forms one of the largest available resource of its kind. They also prove suitability of the result for complex NER evaluation campaigns, including an analysis of the most ambiguous name mentions appearing in the data.

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Using a Language Technology Infrastructure for German in order to Anonymize German Sign Language Corpus Data
Julian Bleicken | Thomas Hanke | Uta Salden | Sven Wagner

For publishing sign language corpus data on the web, anonymization is crucial even if it is impossible to hide the visual appearance of the signers: In a small community, even vague references to third persons may be enough to identify those persons. In the case of the DGS Korpus (German Sign Language corpus) project, we want to publish data as a contribution to the cultural heritage of the sign language community while annotation of the data is still ongoing. This poses the question how well anonymization can be achieved given that no full linguistic analysis of the data is available. Basically, we combine analysis of all data that we have, including named entity recognition on translations into German. For this, we use the WebLicht language technology infrastructure. We report on the reliability of these methods in this special context and also illustrate how the anonymization of the video data is technically achieved in order to minimally disturb the viewer.

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Crowdsourced Corpus with Entity Salience Annotations
Milan Dojchinovski | Dinesh Reddy | Tomáš Kliegr | Tomáš Vitvar | Harald Sack

In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578. The dataset is distributed under a free license and publish in the NLP Interchange Format, which fosters interoperability and re-use. We show the potential of the dataset on the task of learning an entity salience classifier and report on the results from several experiments.

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ELMD: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain
Sergio Oramas | Luis Espinosa Anke | Mohamed Sordo | Horacio Saggion | Xavier Serra

In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain. It contains thousands of musical named entities such as Artist, Song or Record Label, which have been automatically annotated on a set of artist biographies coming from the Music website and social network Last.fm. The annotation process relies on the analysis of the hyperlinks present in the source texts and in a voting-based algorithm for EL, which considers, for each entity mention in text, the degree of agreement across three state-of-the-art EL systems. Manual evaluation shows that EL Precision is at least 94%, and due to its tunable nature, it is possible to derive annotations favouring higher Precision or Recall, at will. We make available the annotated dataset along with evaluation data and the code.

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Bridge-Language Capitalization Inference in Western Iranian: Sorani, Kurmanji, Zazaki, and Tajik
Patrick Littell | David R. Mortensen | Kartik Goyal | Chris Dyer | Lori Levin

In Sorani Kurdish, one of the most useful orthographic features in named-entity recognition – capitalization – is absent, as the language’s Perso-Arabic script does not make a distinction between uppercase and lowercase letters. We describe a system for deriving an inferred capitalization value from closely related languages by phonological similarity, and illustrate the system using several related Western Iranian languages.

We describe a corpus of consumer health questions annotated with named entities. The corpus consists of 1548 de-identified questions about diseases and drugs, written in English. We defined 15 broad categories of biomedical named entities for annotation. A pilot annotation phase in which a small portion of the corpus was double-annotated by four annotators was followed by a main phase in which double annotation was carried out by six annotators, and a reconciliation phase in which all annotations were reconciled by an expert. We conducted the annotation in two modes, manual and assisted, to assess the effect of automatic pre-annotation and calculated inter-annotator agreement. We obtained moderate inter-annotator agreement; assisted annotation yielded slightly better agreement and fewer missed annotations than manual annotation. Due to complex nature of biomedical entities, we paid particular attention to nested entities for which we obtained slightly lower inter-annotator agreement, confirming that annotating nested entities is somewhat more challenging. To our knowledge, the corpus is the first of its kind for consumer health text and is publicly available.

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A Regional News Corpora for Contextualized Entity Discovery and Linking
Adrian Braşoveanu | Lyndon J.B. Nixon | Albert Weichselbraun | Arno Scharl

This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and TAC that have been used to construct this corpus based on news transcripts from the German regional broadcaster RBB (Rundfunk Berlin Brandenburg). Since creating such language resources requires significant effort, the paper also discusses how to derive additional evaluation resources for tasks like named entity contextualization or ontology enrichment by exploiting the links between named entities from the annotated corpus. The paper concludes with an evaluation that shows how several well-known NEL tools perform on the corpus, a discussion of the evaluation results, and with suggestions on how to keep evaluation corpora and datasets up to date.

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DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus
Martin Brümmer | Milan Dojchinovski | Sebastian Hellmann

The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness and relative quality, the Wikipedia has already been a source of such data, but on a limited scale. This paper introduces the DBpedia Abstract Corpus, a large-scale, open corpus of annotated Wikipedia texts in six languages, featuring over 11 million texts and over 97 million entity links. The properties of the Wikipedia texts are being described, as well as the corpus creation process, its format and interesting use-cases, like Named Entity Linking training and evaluation.

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Government Domain Named Entity Recognition for South African Languages
Roald Eiselen

This paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data sets with at least 15,000 annotated named entity tokens for ten of the official South African languages. The description of the protocols and annotated data sets provide an overview of the problems encountered during the annotation of the data sets. Based on these annotated data sets, CRF named entity recognition systems are developed that leverage existing linguistic resources. The newly created named entity recognisers are evaluated, with F-scores of between 0.64 and 0.77, and error analysis is performed to identify possible avenues for improving the quality of the systems.

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Named Entity Resources - Overview and Outlook
Maud Ehrmann | Damien Nouvel | Sophie Rosset

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and the emergence of new types of data (e.g. speech transcriptions, micro-blogging). These pose certainly new challenges which affect not only methods and algorithms but especially linguistic resources. Where do we stand with respect to named entity resources? This paper aims at providing a systematic overview of named entity resources, accounting for qualities such as multilingualism, dynamicity and interoperability, and to identify shortfalls in order to guide future developments.

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Incorporating Lexico-semantic Heuristics into Coreference Resolution Sieves for Named Entity Recognition at Document-level
Marcos Garcia

This paper explores the incorporation of lexico-semantic heuristics into a deterministic Coreference Resolution (CR) system for classifying named entities at document-level. The highest precise sieves of a CR tool are enriched with both a set of heuristics for merging named entities labeled with different classes and also with some constraints that avoid the incorrect merging of similar mentions. Several tests show that this strategy improves both NER labeling and CR. The CR tool can be applied in combination with any system for named entity recognition using the CoNLL format, and brings benefits to text analytics tasks such as Information Extraction. Experiments were carried out in Spanish, using three different NER tools.

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Using Word Embeddings to Translate Named Entities
Octavia-Maria Şulea | Sergiu Nisioi | Liviu P. Dinu

In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors. Inspired by observations in (Mikolov et al., 2013b), which show that training their word vector model on comparable corpora yields comparable vector space representations of those corpora, reducing the problem of translating words to finding a rotation matrix, and results in (Zou et al., 2013), which showed that bilingual word embeddings can improve Chinese Named Entity Recognition (NER) and English to Chinese phrase translation, we use the sentence-aligned English-French EuroParl corpora and show that word embeddings extracted from a merged corpus (corpus resulted from the merger of the two aligned corpora) can be used to NE translation. We extrapolate that word embeddings trained on merged parallel corpora are useful in Named Entity Recognition and Translation tasks for resource-poor languages.

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TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns
Kathrin Eichler | Feiyu Xu | Hans Uszkoreit | Leonhard Hennig | Sebastian Krause

The task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which encode the grammatical relations among the phrases that jointly express relation instances. State-of-the-art weakly supervised approaches to relation extraction typically extract thousands of unique patterns only potentially expressing the target relation. Among these patterns, some are semantically equivalent, but differ in their morphological, lexical-semantic or syntactic form. Some express a relation that entails the target relation. We propose a new approach to structuring extraction patterns by utilizing entailment graphs, hierarchical structures representing entailment relations, and present a novel resource of gold-standard entailment graphs based on a set of patterns automatically acquired using distant supervision. We describe the methodology used for creating the dataset and present statistics of the resource as well as an analysis of inference types underlying the entailment decisions.

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Passing a USA National Bar Exam: a First Corpus for Experimentation
Biralatei Fawei | Adam Wyner | Jeff Pan

Bar exams provide a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a given jurisdiction. The bar provides an excellent benchmark for the performance of legal information systems since passing the bar would arguably signal that the system has acquired key aspects of legal reason on a par with a human lawyer. The paper provides a corpus and experimental results with material derived from a real bar exam, treating the problem as a form of textual entailment from the question to an answer. The providers of the bar exam material set the Gold Standard, which is the answer key. The experiments carried out using the ‘out of the box’ the Excitement Open Platform for textual entailment. The results and evaluation show that the tool can identify wrong answers (non-entailment) with a high F1 score, but it performs poorly in identifying the correct answer (entailment). The results provide a baseline performance measure against which to evaluate future improvements. The reasons for the poor performance are examined, and proposals are made to augment the tool in the future. The corpus facilitates experimentation by other researchers.

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Corpora for Learning the Mutual Relationship between Semantic Relatedness and Textual Entailment
Ngoc Phuoc An Vo | Octavian Popescu

In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgments and we proceed to the manual annotation with SR scores. The RTE 1-4 corpora used in the PASCAL competition fit our need. The annotators worked independently of one each other and they did not have access to the TE judgment during annotation. The intuition that the two annotations are correlated received major support from this experiment and this finding led to a system that uses this information to revise the initial estimates of SR scores. As semantic relatedness is one of the most general and difficult task in natural language processing we expect that future systems will combine different sources of information in order to solve it. Our work suggests that textual entailment plays a quantifiable role in addressing it.

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Can Topic Modelling benefit from Word Sense Information?
Adriana Ferrugento | Hugo Gonçalo Oliveira | Ana Alves | Filipe Rodrigues

This paper proposes a new topic model that exploits word sense information in order to discover less redundant and more informative topics. Word sense information is obtained from WordNet and the discovered topics are groups of synsets, instead of mere surface words. A key feature is that all the known senses of a word are considered, with their probabilities. Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model. However, the obtained results suggest that there are no benefits of enriching LDA with word sense information.

Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users’ content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user’s age and gender from their forum posts. We use a mix of features from a user’s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.

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Comparing Speech and Text Classification on ICNALE
Sergiu Nisioi

In this paper we explore and compare a speech and text classification approach on a corpus of native and non-native English speakers. We experiment on a subset of the International Corpus Network of Asian Learners of English containing the recorded speeches and the equivalent text transcriptions. Our results suggest a high correlation between the spoken and written classification results, showing that native accent is highly correlated with grammatical structures found in text.

Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. We propose a generic method to enrich and extend the use of different expressions as queries in order to improve the acquisition of relevant disease related pages on the web. Our method combines a text mining approach to extract terms from corpora of relevant disease outbreak documents, and domain expert elicitation (Delphi method) to propose expressions and to select relevant combinations between terms obtained with text mining. In this paper we evaluated the performance as queries of a number of expressions obtained with text mining and validated by a domain expert and expressions proposed by a panel of 21 domain experts. We used African swine fever as an infectious animal disease model. The expressions obtained with text mining outperformed as queries the expressions proposed by domain experts. However, domain experts proposed expressions not extracted automatically. Our method is simple to conduct and flexible to adapt to any other animal infectious disease and even in the public health domain.

Privacy concerns have often served as an insurmountable barrier for the production of research and resources in clinical information retrieval (IR). We believe that both clinical IR research innovation and legitimate privacy concerns can be served by the creation of intra-institutional, fully protected resources. In this paper, we provide some principles and tools for IR resource-building in the unique problem setting of patient-level IR, following the tradition of the Cranfield paradigm.

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Annotating and Detecting Medical Events in Clinical Notes
Prescott Klassen | Fei Xia | Meliha Yetisgen

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7% and 91.8%, respectively.

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Speech Synthesis of Code-Mixed Text
Sunayana Sitaram | Alan W Black

Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in. However, in bilingual and multilingual communities, code mixing or code switching occurs in speech, in which speakers switch between languages in the same utterance. Due to the popularity of social media, we now see code-mixing even in text in these multilingual communities. TTS systems capable of synthesizing such text need to be able to handle text that is written in multiple languages and scripts. Code-mixed text poses many challenges to TTS systems, such as language identification, spelling normalization and pronunciation modeling. In this work, we describe a preliminary framework for synthesizing code-mixed text. We carry out experiments on synthesizing code-mixed Hindi and English text. We find that there is a significant user preference for TTS systems that can correctly identify and pronounce words in different languages.

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Chatbot Technology with Synthetic Voices in the Acquisition of an Endangered Language: Motivation, Development and Evaluation of a Platform for Irish
Neasa Ní Chiaráin | Ailbhe Ní Chasaide

This paper describes the development and evaluation of a chatbot platform designed for the teaching/learning of Irish. The chatbot uses synthetic voices developed for the dialects of Irish. Speech-enabled chatbot technology offers a potentially powerful tool for dealing with the challenges of teaching/learning an endangered language where learners have limited access to native speaker models of the language and limited exposure to the language in a truly communicative setting. The sociolinguistic context that motivates the present development is explained. The evaluation of the chatbot was carried out in 13 schools by 228 pupils and consisted of two parts. Firstly, learners’ opinions of the overall chatbot platform as a learning environment were elicited. Secondly, learners evaluated the intelligibility, quality, and attractiveness of the synthetic voices used in this platform. Results were overwhelmingly positive to both the learning platform and the synthetic voices and indicate that the time may now be ripe for language learning applications which exploit speech and language technologies. It is further argued that these technologies have a particularly vital role to play in the maintenance of the endangered language.

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CHATR the Corpus; a 20-year-old archive of Concatenative Speech Synthesis
Nick Campbell

This paper reports the preservation of an old speech synthesis website as a corpus. CHATR was a revolutionary technique developed in the mid nineties for concatenative speech synthesis. The method has since become the standard for high quality speech output by computer although much of the current research is devoted to parametric or hybrid methods that employ smaller amounts of data and can be more easily tunable to individual voices. The system was first reported in 1994 and the website was functional in 1996. The ATR labs where this system was invented no longer exist, but the website has been preserved as a corpus containing 1537 samples of synthesised speech from that period (118 MB in aiff format) in 211 pages under various finely interrelated themes The corpus can be accessed from www.speech-data.jp as well as www.tcd-fastnet.com, where the original code and samples are now being maintained.

In order to explore intuitive verbal and non-verbal interfaces in smart environments we recorded user interactions with an intelligent apartment. Besides offering various interactive capabilities itself, the apartment is also inhabited by a social robot that is available as a humanoid interface. This paper presents a multi-modal corpus that contains goal-directed actions of naive users in attempts to solve a number of predefined tasks. Alongside audio and video recordings, our data-set consists of large amount of temporally aligned sensory data and system behavior provided by the environment and its interactive components. Non-verbal system responses such as changes in light or display contents, as well as robot and apartment utterances and gestures serve as a rich basis for later in-depth analysis. Manual annotations provide further information about meta data like the current course of study and user behavior including the incorporated modality, all literal utterances, language features, emotional expressions, foci of attention, and addressees.

Story-telling is a fundamental and prevalent aspect of human social behavior. In the wild, stories are told conversationally in social settings, often as a dialogue and with accompanying gestures and other nonverbal behavior. This paper presents a new corpus, the Story Dialogue with Gestures (SDG) corpus, consisting of 50 personal narratives regenerated as dialogues, complete with annotations of gesture placement and accompanying gesture forms. The corpus includes dialogues generated by human annotators, gesture annotations on the human generated dialogues, videos of story dialogues generated from this representation, video clips of each gesture used in the gesture annotations, and annotations of the original personal narratives with a deep representation of story called a Story Intention Graph. Our long term goal is the automatic generation of story co-tellings as animated dialogues from the Story Intention Graph. We expect this corpus to be a useful resource for researchers interested in natural language generation, intelligent virtual agents, generation of nonverbal behavior, and story and narrative representations.

This paper reports on work related to the modelling of Human-Robot Communication on the basis of multimodal and multisensory human behaviour analysis. A primary focus in this framework of analysis is the definition of semantics of human actions in interaction, their capture and their representation in terms of behavioural patterns that, in turn, feed a multimodal human-robot communication system. Semantic analysis encompasses both oral and sign languages, as well as both verbal and non-verbal communicative signals to achieve an effective, natural interaction between elderly users with slight walking and cognitive inability and an assistive robotic platform.

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A Verbal and Gestural Corpus of Story Retellings to an Expressive Embodied Virtual Character
Jackson Tolins | Kris Liu | Michael Neff | Marilyn Walker | Jean Fox Tree

We present a corpus of 44 human-agent verbal and gestural story retellings designed to explore whether humans would gesturally entrain to an embodied intelligent virtual agent. We used a novel data collection method where an agent presented story components in installments, which the human would then retell to the agent. At the end of the installments, the human would then retell the embodied animated agent the story as a whole. This method was designed to allow us to observe whether changes in the agent’s gestural behavior would result in human gestural changes. The agent modified its gestures over the course of the story, by starting out the first installment with gestural behaviors designed to manifest extraversion, and slowly modifying gestures to express introversion over time, or the reverse. The corpus contains the verbal and gestural transcripts of the human story retellings. The gestures were coded for type, handedness, temporal structure, spatial extent, and the degree to which the participants’ gestures match those produced by the agent. The corpus illustrates the variation in expressive behaviors produced by users interacting with embodied virtual characters, and the degree to which their gestures were influenced by the agent’s dynamic changes in personality-based expressive style.

This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues). Participants were selected from a larger sample to be 0.8 of a standard deviation above or below the mean on the Big-Five Personality extraversion scale, to produce an Extravert-Extravert dyad, an Introvert-Introvert dyad, and an Extravert-Introvert dyad. Each pair carried out conversations for three different tasks. The conversations were recorded using optical motion capture for the body and data gloves for the hands. Dyads’ speech was transcribed and the gestural and postural behavior was annotated with ANVIL. The released corpus includes personality profiles, ANVIL files containing speech transcriptions and the gestural annotations, and BVH files containing body and hand motion in 3D.

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Crowdsourcing Ontology Lexicons
Bettina Lanser | Christina Unger | Philipp Cimiano

In order to make the growing amount of conceptual knowledge available through ontologies and datasets accessible to humans, NLP applications need access to information on how this knowledge can be verbalized in natural language. One way to provide this kind of information are ontology lexicons, which apart from the actual verbalizations in a given target language can provide further, rich linguistic information about them. Compiling such lexicons manually is a very time-consuming task and requires expertise both in Semantic Web technologies and lexicon engineering, as well as a very good knowledge of the target language at hand. In this paper we present an alternative approach to generating ontology lexicons by means of crowdsourcing: We use CrowdFlower to generate a small Japanese ontology lexicon for ten exemplary ontology elements from the DBpedia ontology according to a two-stage workflow, the main underlying idea of which is to turn the task of generating lexicon entries into a translation task; the starting point of this translation task is a manually created English lexicon for DBpedia. Comparison of the results to a manually created Japanese lexicon shows that the presented workflow is a viable option if an English seed lexicon is already available.

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InScript: Narrative texts annotated with script information
Ashutosh Modi | Tatjana Anikina | Simon Ostermann | Manfred Pinkal

This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.

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A Crowdsourced Database of Event Sequence Descriptions for the Acquisition of High-quality Script Knowledge
Lilian D. A. Wanzare | Alessandra Zarcone | Stefan Thater | Manfred Pinkal

Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a large-scale crowdsourced collection of explicit linguistic descriptions of script-specific event sequences (40 scenarios with 100 sequences each). The corpus is enriched with crowdsourced alignment annotation on a subset of the event descriptions, to be used in future work as seed data for automatic alignment of event descriptions (for example via clustering). The event descriptions to be aligned were chosen among those expected to have the strongest corrective effect on the clustering algorithm. The alignment annotation was evaluated against a gold standard of expert annotators. The resulting database of partially-aligned script-event descriptions provides a sound empirical basis for inducing high-quality script knowledge, as well as for any task involving alignment and paraphrase detection of events.

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Temporal Information Annotation: Crowd vs. Experts
Tommaso Caselli | Rachele Sprugnoli | Oana Inel

This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i.e., English and Italian. The first experiment, launched on the CrowdFlower platform, was aimed at classifying temporal relations given target entities. The second one, relying on the CrowdTruth metric, consisted in two subtasks: one devoted to the recognition of events and temporal expressions and one to the detection and classification of temporal relations. The outcomes of the experiments suggest a valuable use of crowdsourcing annotations also for a complex task like Temporal Processing.

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A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C)
Franco Salvetti | John B. Lowe | James H. Martin

We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of reviews built using this approach, available free of charge from LDC as the Boulder Lies and Truth Corpus (BLT-C). Challenges for both corpus creation and the deception detection include the fact that human performance on the task is typically at chance, that the signal is faint, that paid writers such as turkers are sometimes deceptive, and that deception is a complex human behavior; manifestations of deception depend on details of domain, intrinsic properties of the deceiver (such as education, linguistic competence, and the nature of the intention), and specifics of the deceptive act (e.g., lying vs. fabricating.) To overcome the inherent lack of ground truth, we have developed a set of semi-automatic techniques to ensure corpus validity. We present some preliminary results on the task of deception detection which suggest that the BLT-C is an improvement in the quality of resources available for this task.

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Finding Alternative Translations in a Large Corpus of Movie Subtitle
Jörg Tiedemann

OpenSubtitles.org provides a large collection of user contributed subtitles in various languages for movies and TV programs. Subtitle translations are valuable resources for cross-lingual studies and machine translation research. A less explored feature of the collection is the inclusion of alternative translations, which can be very useful for training paraphrase systems or collecting multi-reference test suites for machine translation. However, differences in translation may also be due to misspellings, incomplete or corrupt data files, or wrongly aligned subtitles. This paper reports our efforts in recognising and classifying alternative subtitle translations with language independent techniques. We use time-based alignment with lexical re-synchronisation techniques and BLEU score filters and sort alternative translations into categories using edit distance metrics and heuristic rules. Our approach produces large numbers of sentence-aligned translation alternatives for over 50 languages provided via the OPUS corpus collection.

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Exploiting a Large Strongly Comparable Corpus
Thierry Etchegoyhen | Andoni Azpeitia | Naiara Pérez

This article describes a large comparable corpus for Basque and Spanish and the methods employed to build a parallel resource from the original data. The EITB corpus, a strongly comparable corpus in the news domain, is to be shared with the research community, as an aid for the development and testing of methods in comparable corpora exploitation, and as basis for the improvement of data-driven machine translation systems for this language pair. Competing approaches were explored for the alignment of comparable segments in the corpus, resulting in the design of a simple method which outperformed a state-of-the-art method on the corpus test sets. The method we present is highly portable, computationally efficient, and significantly reduces deployment work, a welcome result for the exploitation of comparable corpora.

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The United Nations Parallel Corpus v1.0
Michał Ziemski | Marcin Junczys-Dowmunt | Bruno Pouliquen

This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.

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WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
Luisa Bentivogli | Mauro Cettolo | M. Amin Farajian | Marcello Federico

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3% of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems’ performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.

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Manual and Automatic Paraphrases for MT Evaluation
Aleš Tamchyna | Petra Barančíková

Paraphrasing of reference translations has been shown to improve the correlation with human judgements in automatic evaluation of machine translation (MT) outputs. In this work, we present a new dataset for evaluating English-Czech translation based on automatic paraphrases. We compare this dataset with an existing set of manually created paraphrases and find that even automatic paraphrases can improve MT evaluation. We have also propose and evaluate several criteria for selecting suitable reference translations from a larger set.

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Poly-GrETEL: Cross-Lingual Example-based Querying of Syntactic Constructions
Liesbeth Augustinus | Vincent Vandeghinste | Tom Vanallemeersch

We present Poly-GrETEL, an online tool which enables syntactic querying in parallel treebanks, based on the monolingual GrETEL environment. We provide online access to the Europarl parallel treebank for Dutch and English, allowing users to query the treebank using either an XPath expression or an example sentence in order to look for similar constructions. We provide automatic alignments between the nodes. By combining example-based query functionality with node alignments, we limit the need for users to be familiar with the query language and the structure of the trees in the source and target language, thus facilitating the use of parallel corpora for comparative linguistics and translation studies.

We present NorGramBank, a treebank for Norwegian with highly detailed LFG analyses. It is one of many treebanks made available through the INESS treebanking infrastructure. NorGramBank was constructed as a parsebank, i.e. by automatically parsing a corpus, using the wide coverage grammar NorGram. One part consisting of 350,000 words has been manually disambiguated using computer-generated discriminants. A larger part of 50 M words has been stochastically disambiguated. The treebank is dynamic: by global reparsing at certain intervals it is kept compatible with the latest versions of the grammar and the lexicon, which are continually further developed in interaction with the annotators. A powerful query language, INESS Search, has been developed for search across formalisms in the INESS treebanks, including LFG c- and f-structures. Evaluation shows that the grammar provides about 85% of randomly selected sentences with good analyses. Agreement among the annotators responsible for manual disambiguation is satisfactory, but also suggests desirable simplifications of the grammar.

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Accurate Deep Syntactic Parsing of Graphs: The Case of French
Corentin Ribeyre | Eric Villemonte de la Clergerie | Djamé Seddah

Parsing predicate-argument structures in a deep syntax framework requires graphs to be predicted. Argument structures represent a higher level of abstraction than the syntactic ones and are thus more difficult to predict even for highly accurate parsing models on surfacic syntax. In this paper we investigate deep syntax parsing, using a French data set (Ribeyre et al., 2014a). We demonstrate that the use of topologically different types of syntactic features, such as dependencies, tree fragments, spines or syntactic paths, brings a much needed context to the parser. Our higher-order parsing model, gaining thus up to 4 points, establishes the state of the art for parsing French deep syntactic structures.

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Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic
Abdelati Hawwari | Mohammed Attia | Mahmoud Ghoneim | Mona Diab

Idafa in traditional Arabic grammar is an umbrella construction that covers several phenomena including what is expressed in English as noun-noun compounds and Saxon and Norman genitives. Additionally, Idafa participates in some other constructions, such as quantifiers, quasi-prepositions, and adjectives. Identifying the various types of the Idafa construction (IC) is of importance to Natural Language processing (NLP) applications. Noun-Noun compounds exhibit special behavior in most languages impacting their semantic interpretation. Hence distinguishing them could have an impact on downstream NLP applications. The most comprehensive syntactic representation of the Arabic language is the LDC Arabic Treebank (ATB). In the ATB, ICs are not explicitly labeled and furthermore, there is no distinction between ICs of noun-noun relations and other traditional ICs. Hence, we devise a detailed syntactic and semantic typification process of the IC phenomenon in Arabic. We target the ATB as a platform for this classification. We render the ATB annotated with explicit IC labels but with the further semantic characterization which is useful for syntactic, semantic and cross language processing. Our typification of IC comprises 3 main syntactic IC types: FIC, GIC, and TIC, and they are further divided into 10 syntactic subclasses. The TIC group is further classified into semantic relations. We devise a method for automatic IC labeling and compare its yield against the CATiB treebank. Our evaluation shows that we achieve the same level of accuracy, but with the additional fine-grained classification into the various syntactic and semantic types.

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Compasses, Magnets, Water Microscopes: Annotation of Terminology in a Diachronic Corpus of Scientific Texts
Anne-Kathrin Schumann | Stefan Fischer

The specialised lexicon belongs to the most prominent attributes of specialised writing: Terms function as semantically dense encodings of specialised concepts, which, in the absence of terms, would require lengthy explanations and descriptions. In this paper, we argue that terms are the result of diachronic processes on both the semantic and the morpho-syntactic level. Very little is known about these processes. We therefore present a corpus annotation project aiming at revealing how terms are coined and how they evolve to fit their function as semantically and morpho-syntactically dense encodings of specialised knowledge. The scope of this paper is two-fold: Firstly, we outline our methodology for annotating terminology in a diachronic corpus of scientific publications. Moreover, we provide a detailed analysis of our annotation results and suggest methods for improving the accuracy of annotations in a setting as difficult as ours. Secondly, we present results of a pilot study based on the annotated terms. The results suggest that terms in older texts are linguistically relatively simple units that are hard to distinguish from the lexicon of general language. We believe that this supports our hypothesis that terminology undergoes diachronic processes of densification and specialisation.

KorAP is a corpus search and analysis platform, developed at the Institute for the German Language (IDS). It supports very large corpora with multiple annotation layers, multiple query languages, and complex licensing scenarios. KorAP’s design aims to be scalable, flexible, and sustainable to serve the German Reference Corpus DeReKo for at least the next decade. To meet these requirements, we have adopted a highly modular microservice-based architecture. This paper outlines our approach: An architecture consisting of small components that are easy to extend, replace, and maintain. The components include a search backend, a user and corpus license management system, and a web-based user frontend. We also describe a general corpus query protocol used by all microservices for internal communications. KorAP is open source, licensed under BSD-2, and available on GitHub.

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Text Segmentation of Digitized Clinical Texts
Cyril Grouin

In this paper, we present the experiments we made to recover the original page layout structure into two columns from layout damaged digitized files. We designed several CRF-based approaches, either to identify column separator or to classify each token from each line into left or right columns. We achieved our best results with a model trained on homogeneous corpora (only files composed of 2 columns) when classifying each token into left or right columns (overall F-measure of 0.968). Our experiments show it is possible to recover the original layout in columns of digitized documents with results of quality.

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A Turkish Database for Psycholinguistic Studies Based on Frequency, Age of Acquisition, and Imageability
Elif Ahsen Acar | Deniz Zeyrek | Murathan Kurfalı | Cem Bozşahin

This study primarily aims to build a Turkish psycholinguistic database including three variables: word frequency, age of acquisition (AoA), and imageability, where AoA and imageability information are limited to nouns. We used a corpus-based approach to obtain information about the AoA variable. We built two corpora: a child literature corpus (CLC) including 535 books written for 3-12 years old children, and a corpus of transcribed children’s speech (CSC) at ages 1;4-4;8. A comparison between the word frequencies of CLC and CSC gave positive correlation results, suggesting the usability of the CLC to extract AoA information. We assumed that frequent words of the CLC would correspond to early acquired words whereas frequent words of a corpus of adult language would correspond to late acquired words. To validate AoA results from our corpus-based approach, a rated AoA questionnaire was conducted on adults. Imageability values were collected via a different questionnaire conducted on adults. We conclude that it is possible to deduce AoA information for high frequency words with the corpus-based approach. The results about low frequency words were inconclusive, which is attributed to the fact that corpus-based AoA information is affected by the strong negative correlation between corpus frequency and rated AoA.

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Domain-Specific Corpus Expansion with Focused Webcrawling
Steffen Remus | Chris Biemann

This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software.

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Corpus-Based Diacritic Restoration for South Slavic Languages
Nikola Ljubešić | Tomaž Erjavec | Darja Fišer

In computer-mediated communication, Latin-based scripts users often omit diacritics when writing. Such text is typically easily understandable to humans but very difficult for computational processing because many words become ambiguous or unknown. Letter-level approaches to diacritic restoration generalise better and do not require a lot of training data but word-level approaches tend to yield better results. However, they typically rely on a lexicon which is an expensive resource, not covering non-standard forms, and often not available for less-resourced languages. In this paper we present diacritic restoration models that are trained on easy-to-acquire corpora. We test three different types of corpora (Wikipedia, general web, Twitter) for three South Slavic languages (Croatian, Serbian and Slovene) and evaluate them on two types of text: standard (Wikipedia) and non-standard (Twitter). The proposed approach considerably outperforms charlifter, so far the only open source tool available for this task. We make the best performing systems freely available.

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Automatic Recognition of Linguistic Replacements in Text Series Generated from Keystroke Logs
Daniel Couto-Vale | Stella Neumann | Paula Niemietz

This paper introduces a toolkit used for the purpose of detecting replacements of different grammatical and semantic structures in ongoing text production logged as a chronological series of computer interaction events (so-called keystroke logs). The specific case we use involves human translations where replacements can be indicative of translator behaviour that leads to specific features of translations that distinguish them from non-translated texts. The toolkit uses a novel CCG chart parser customised so as to recognise grammatical words independently of space and punctuation boundaries. On the basis of the linguistic analysis, structures in different versions of the target text are compared and classified as potential equivalents of the same source text segment by ‘equivalence judges’. In that way, replacements of grammatical and semantic structures can be detected. Beyond the specific task at hand the approach will also be useful for the analysis of other types of spaceless text such as Twitter hashtags and texts in agglutinative or spaceless languages like Finnish or Chinese.

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Automatic Corpus Extension for Data-driven Natural Language Generation
Elena Manishina | Bassam Jabaian | Stéphane Huet | Fabrice Lefèvre

As data-driven approaches started to make their way into the Natural Language Generation (NLG) domain, the need for automation of corpus building and extension became apparent. Corpus creation and extension in data-driven NLG domain traditionally involved manual paraphrasing performed by either a group of experts or with resort to crowd-sourcing. Building the training corpora manually is a costly enterprise which requires a lot of time and human resources. We propose to automate the process of corpus extension by integrating automatically obtained synonyms and paraphrases. Our methodology allowed us to significantly increase the size of the training corpus and its level of variability (the number of distinct tokens and specific syntactic structures). Our extension solutions are fully automatic and require only some initial validation. The human evaluation results confirm that in many cases native speakers favor the outputs of the model built on the extended corpus.

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Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers
Amal Htait | Sebastien Fournier | Patrice Bellot

In this paper, we present the automatic annotation of bibliographical references’ zone in papers and articles of XML/TEI format. Our work is applied through two phases: first, we use machine learning technology to classify bibliographical and non-bibliographical paragraphs in papers, by means of a model that was initially created to differentiate between the footnotes containing or not containing bibliographical references. The previous description is one of BILBO’s features, which is an open source software for automatic annotation of bibliographic reference. Also, we suggest some methods to minimize the margin of error. Second, we propose an algorithm to find the largest list of bibliographical references in the article. The improvement applied on our model results an increase in the model’s efficiency with an Accuracy equal to 85.89. And by testing our work, we are able to achieve 72.23% as an average for the percentage of success in detecting bibliographical references’ zone.

This paper presents the annotation guidelines developed as part of an effort to create a large scale manually diacritized corpus for various Arabic text genres. The target size of the annotated corpus is 2 million words. We summarize the guidelines and describe issues encountered during the training of the annotators. We also discuss the challenges posed by the complexity of the Arabic language and how they are addressed. Finally, we present the diacritization annotation procedure and detail the quality of the resulting annotations.

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Applying the Cognitive Machine Translation Evaluation Approach to Arabic
Irina Temnikova | Wajdi Zaghouani | Stephan Vogel | Nizar Habash

The goal of the cognitive machine translation (MT) evaluation approach is to build classifiers which assign post-editing effort scores to new texts. The approach helps estimate fair compensation for post-editors in the translation industry by evaluating the cognitive difficulty of post-editing MT output. The approach counts the number of errors classified in different categories on the basis of how much cognitive effort they require in order to be corrected. In this paper, we present the results of applying an existing cognitive evaluation approach to Modern Standard Arabic (MSA). We provide a comparison of the number of errors and categories of errors in three MSA texts of different MT quality (without any language-specific adaptation), as well as a comparison between MSA texts and texts from three Indo-European languages (Russian, Spanish, and Bulgarian), taken from a previous experiment. The results show how the error distributions change passing from the MSA texts of worse MT quality to MSA texts of better MT quality, as well as a similarity in distinguishing the texts of better MT quality for all four languages.

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A Reading Comprehension Corpus for Machine Translation Evaluation
Carolina Scarton | Lucia Specia

Effectively assessing Natural Language Processing output tasks is a challenge for research in the area. In the case of Machine Translation (MT), automatic metrics are usually preferred over human evaluation, given time and budget constraints. However, traditional automatic metrics (such as BLEU) are not reliable for absolute quality assessment of documents, often producing similar scores for documents translated by the same MT system. For scenarios where absolute labels are necessary for building models, such as document-level Quality Estimation, these metrics can not be fully trusted. In this paper, we introduce a corpus of reading comprehension tests based on machine translated documents, where we evaluate documents based on answers to questions by fluent speakers of the target language. We describe the process of creating such a resource, the experiment design and agreement between the test takers. Finally, we discuss ways to convert the reading comprehension test into document-level quality scores.

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B2SG: a TOEFL-like Task for Portuguese
Rodrigo Wilkens | Leonardo Zilio | Eduardo Ferreira | Aline Villavicencio

Resources such as WordNet are useful for NLP applications, but their manual construction consumes time and personnel, and frequently results in low coverage. One alternative is the automatic construction of large resources from corpora like distributional thesauri, containing semantically associated words. However, as they may contain noise, there is a strong need for automatic ways of evaluating the quality of the resulting resource. This paper introduces a gold standard that can aid in this task. The BabelNet-Based Semantic Gold Standard (B2SG) was automatically constructed based on BabelNet and partly evaluated by human judges. It consists of sets of tests that present one target word, one related word and three unrelated words. B2SG contains 2,875 validated relations: 800 for verbs and 2,075 for nouns; these relations are divided among synonymy, antonymy and hypernymy. They can be used as the basis for evaluating the accuracy of the similarity relations on distributional thesauri by comparing the proximity of the target word with the related and unrelated options and observing if the related word has the highest similarity value among them. As a case study two distributional thesauri were also developed: one using surface forms from a large (1.5 billion word) corpus and the other using lemmatized forms from a smaller (409 million word) corpus. Both distributional thesauri were then evaluated against B2SG, and the one using lemmatized forms performed slightly better.

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MoBiL: A Hybrid Feature Set for Automatic Human Translation Quality Assessment
Yu Yuan | Serge Sharoff | Bogdan Babych

In this paper we introduce MoBiL, a hybrid Monolingual, Bilingual and Language modelling feature set and feature selection and evaluation framework. The set includes translation quality indicators that can be utilized to automatically predict the quality of human translations in terms of content adequacy and language fluency. We compare MoBiL with the QuEst baseline set by using them in classifiers trained with support vector machine and relevance vector machine learning algorithms on the same data set. We also report an experiment on feature selection to opt for fewer but more informative features from MoBiL. Our experiments show that classifiers trained on our feature set perform consistently better in predicting both adequacy and fluency than the classifiers trained on the baseline feature set. MoBiL also performs well when used with both support vector machine and relevance vector machine algorithms.

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MARMOT: A Toolkit for Translation Quality Estimation at the Word Level
Varvara Logacheva | Chris Hokamp | Lucia Specia

We present Marmot~― a new toolkit for quality estimation (QE) of machine translation output. Marmot contains utilities targeted at quality estimation at the word and phrase level. However, due to its flexibility and modularity, it can also be extended to work at the sentence level. In addition, it can be used as a framework for extracting features and learning models for many common natural language processing tasks. The tool has a set of state-of-the-art features for QE, and new features can easily be added. The tool is open-source and can be downloaded from https://github.com/qe-team/marmot/

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RankDCG: Rank-Ordering Evaluation Measure
Denys Katerenchuk | Andrew Rosenberg

Ranking is used for a wide array of problems, most notably information retrieval (search). Kendall’s τ, Average Precision, and nDCG are a few popular approaches to the evaluation of ranking. When dealing with problems such as user ranking or recommendation systems, all these measures suffer from various problems, including the inability to deal with elements of the same rank, inconsistent and ambiguous lower bound scores, and an inappropriate cost function. We propose a new measure, a modification of the popular nDCG algorithm, named rankDCG, that addresses these problems. We provide a number of criteria for any effective ranking algorithm and show that only rankDCG satisfies them all. Results are presented on constructed and real data sets. We release a publicly available rankDCG evaluation package.

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Spanish Word Vectors from Wikipedia
Mathias Etcheverry | Dina Wonsever

Contents analisys from text data requires semantic representations that are difficult to obtain automatically, as they may require large handcrafted knowledge bases or manually annotated examples. Unsupervised autonomous methods for generating semantic representations are of greatest interest in face of huge volumes of text to be exploited in all kinds of applications. In this work we describe the generation and validation of semantic representations in the vector space paradigm for Spanish. The method used is GloVe (Pennington, 2014), one of the best performing reported methods , and vectors were trained over Spanish Wikipedia. The learned vectors evaluation is done in terms of word analogy and similarity tasks (Pennington, 2014; Baroni, 2014; Mikolov, 2013a). The vector set and a Spanish version for some widely used semantic relatedness tests are made publicly available.

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Analyzing Pre-processing Settings for Urdu Single-document Extractive Summarization
Muhammad Humayoun | Hwanjo Yu

Preprocessing is a preliminary step in many fields including IR and NLP. The effect of basic preprocessing settings on English for text summarization is well-studied. However, there is no such effort found for the Urdu language (with the best of our knowledge). In this study, we analyze the effect of basic preprocessing settings for single-document text summarization for Urdu, on a benchmark corpus using various experiments. The analysis is performed using the state-of-the-art algorithms for extractive summarization and the effect of stopword removal, lemmatization, and stemming is analyzed. Results showed that these pre-processing settings improve the results.

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Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature
Kata Gábor | Haïfa Zargayouna | Davide Buscaldi | Isabelle Tellier | Thierry Charnois

This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpora. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.

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GATE-Time: Extraction of Temporal Expressions and Events
Leon Derczynski | Jannik Strötgen | Diana Maynard | Mark A. Greenwood | Manuel Jung

GATE is a widely used open-source solution for text processing with a large user community. It contains components for several natural language processing tasks. However, temporal information extraction functionality within GATE has been rather limited so far, despite being a prerequisite for many application scenarios in the areas of natural language processing and information retrieval. This paper presents an integrated approach to temporal information processing. We take state-of-the-art tools in temporal expression and event recognition and bring them together to form an openly-available resource within the GATE infrastructure. GATE-Time provides annotation in the form of TimeML events and temporal expressions complying with this mature ISO standard for temporal semantic annotation of documents. Major advantages of GATE-Time are (i) that it relies on HeidelTime for temporal tagging, so that temporal expressions can be extracted and normalized in multiple languages and across different domains, (ii) it includes a modern, fast event recognition and classification tool, and (iii) that it can be combined with different linguistic pre-processing annotations, and is thus not bound to license restricted preprocessing components.

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Distributional Thesauri for Information Retrieval and vice versa
Vincent Claveau | Ewa Kijak

Distributional thesauri are useful in many tasks of Natural Language Processing. In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval (IR) concepts. Two main contributions are proposed. First, following the work of [8], we show how IR tools and concepts can be used with success to build a thesaurus. Through several experiments and by evaluating directly the results with reference lexicons, we show that some IR models outperform state-of-the-art systems. Secondly, we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validates the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences bring these evaluation habits into question.

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Parallel Chinese-English Entities, Relations and Events Corpora
Justin Mott | Ann Bies | Zhiyi Song | Stephanie Strassel

This paper introduces the parallel Chinese-English Entities, Relations and Events (ERE) corpora developed by Linguistic Data Consortium under the DARPA Deep Exploration and Filtering of Text (DEFT) Program. Original Chinese newswire and discussion forum documents are annotated for two versions of the ERE task. The texts are manually translated into English and then annotated for the same ERE tasks on the English translation, resulting in a rich parallel resource that has utility for performers within the DEFT program, for participants in NIST’s Knowledge Base Population evaluations, and for cross-language projection research more generally.

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The PsyMine Corpus - A Corpus annotated with Psychiatric Disorders and their Etiological Factors
Tilia Ellendorff | Simon Foster | Fabio Rinaldi

We present the first version of a corpus annotated for psychiatric disorders and their etiological factors. The paper describes the choice of text, annotated entities and events/relations as well as the annotation scheme and procedure applied. The corpus is featuring a selection of focus psychiatric disorders including depressive disorder, anxiety disorder, obsessive-compulsive disorder, phobic disorders and panic disorder. Etiological factors for these focus disorders are widespread and include genetic, physiological, sociological and environmental factors among others. Etiological events, including annotated evidence text, represent the interactions between their focus disorders and their etiological factors. Additionally to these core events, symptomatic and treatment events have been annotated. The current version of the corpus includes 175 scientific abstracts. All entities and events/relations have been manually annotated by domain experts and scores of inter-annotator agreement are presented. The aim of the corpus is to provide a first gold standard to support the development of biomedical text mining applications for the specific area of mental disorders which belong to the main contributors to the contemporary burden of disease.

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An Empirical Exploration of Moral Foundations Theory in Partisan News Sources
Dean Fulgoni | Jordan Carpenter | Lyle Ungar | Daniel Preoţiuc-Pietro

News sources frame issues in different ways in order to appeal or control the perception of their readers. We present a large scale study of news articles from partisan sources in the US across a variety of different issues. We first highlight that differences between sides exist by predicting the political leaning of articles of unseen political bias. Framing can be driven by different types of morality that each group values. We emphasize differences in framing of different news building on the moral foundations theory quantified using hand crafted lexicons. Our results show that partisan sources frame political issues differently both in terms of words usage and through the moral foundations they relate to.

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Building a Dataset for Possessions Identification in Text
Carmen Banea | Xi Chen | Rada Mihalcea

Just as industrialization matured from mass production to customization and personalization, so has the Web migrated from generic content to public disclosures of one’s most intimately held thoughts, opinions and beliefs. This relatively new type of data is able to represent finer and more narrowly defined demographic slices. If until now researchers have primarily focused on leveraging personalized content to identify latent information such as gender, nationality, location, or age of the author, this study seeks to establish a structured way of extracting possessions, or items that people own or are entitled to, as a way to ultimately provide insights into people’s behaviors and characteristics. In order to promote more research in this area, we are releasing a set of 798 possessions extracted from blog genre, where possessions are marked at different confidence levels, as well as a detailed set of guidelines to help in future annotation studies.

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The Query of Everything: Developing Open-Domain, Natural-Language Queries for BOLT Information Retrieval
Kira Griffitt | Stephanie Strassel

The DARPA BOLT Information Retrieval evaluations target open-domain natural-language queries over a large corpus of informal text in English, Chinese and Egyptian Arabic. We outline the goals of BOLT IR, comparing it with the prior GALE Distillation task. After discussing the properties of the BOLT IR corpus, we provide a detailed description of the query creation process, contrasting the summary query format presented to systems at run time with the full query format created by annotators. We describe the relevance criteria used to assess BOLT system responses, highlighting the evolution of the procedures used over the three evaluation phases. We provide a detailed review of the decision points model for relevance assessment introduced during Phase 2, and conclude with information about inter-assessor consistency achieved with the decision points assessment model.

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The Validation of MRCPD Cross-language Expansions on Imageability Ratings
Ting Liu | Kit Cho | Tomek Strzalkowski | Samira Shaikh | Mehrdad Mirzaei

In this article, we present a method to validate a multi-lingual (English, Spanish, Russian, and Farsi) corpus on imageability ratings automatically expanded from MRCPD (Liu et al., 2014). We employed the corpus (Brysbaert et al., 2014) on concreteness ratings for our English MRCPD+ validation because of lacking human assessed imageability ratings and high correlation between concreteness ratings and imageability ratings (e.g. r = .83). For the same reason, we built a small corpus with human imageability assessment for the other language corpus validation. The results show that the automatically expanded imageability ratings are highly correlated with human assessment in all four languages, which demonstrate our automatic expansion method is valid and robust. We believe these new resources can be of significant interest to the research community, particularly in natural language processing and computational sociolinguistics.

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Building Tempo-HindiWordNet: A resource for effective temporal information access in Hindi
Dipawesh Pawar | Mohammed Hasanuzzaman | Asif Ekbal

In this paper, we put forward a strategy that supplements Hindi WordNet entries with information on the temporality of its word senses. Each synset of Hindi WordNet is automatically annotated to one of the five dimensions: past, present, future, neutral and atemporal. We use semi-supervised learning strategy to build temporal classifiers over the glosses of manually selected initial seed synsets. The classification process is iterated based on the repetitive confidence based expansion strategy of the initial seed list until cross-validation accuracy drops. The resource is unique in its nature as, to the best of our knowledge, still no such resource is available for Hindi.

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Detection of Reformulations in Spoken French
Natalia Grabar | Iris Eshkol-Taravela

Our work addresses automatic detection of enunciations and segments with reformulations in French spoken corpora. The proposed approach is syntagmatic. It is based on reformulation markers and specificities of spoken language. The reference data are built manually and have gone through consensus. Automatic methods, based on rules and CRF machine learning, are proposed in order to detect the enunciations and segments that contain reformulations. With the CRF models, different features are exploited within a window of various sizes. Detection of enunciations with reformulations shows up to 0.66 precision. The tests performed for the detection of reformulated segments indicate that the task remains difficult. The best average performance values reach up to 0.65 F-measure, 0.75 precision, and 0.63 recall. We have several perspectives to this work for improving the detection of reformulated segments and for studying the data from other points of view.

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DT-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context
Rajendra Banjade | Vasile Rus

Negation is often found more frequent in dialogue than commonly written texts, such as literary texts. Furthermore, the scope and focus of negation depends on context in dialogues than other forms of texts. Existing negation datasets have focused on non-dialogue texts such as literary texts where the scope and focus of negation is normally present within the same sentence where the negation is located and therefore are not the most appropriate to inform the development of negation handling algorithms for dialogue-based systems. In this paper, we present DT -Neg corpus (DeepTutor Negation corpus) which contains texts extracted from tutorial dialogues where students interacted with an Intelligent Tutoring System (ITS) to solve conceptual physics problems. The DT -Neg corpus contains annotated negations in student responses with scope and focus marked based on the context of the dialogue. Our dataset contains 1,088 instances and is available for research purposes at http://language.memphis.edu/dt-neg.

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Annotating Logical Forms for EHR Questions
Kirk Roberts | Dina Demner-Fushman

This paper discusses the creation of a semantically annotated corpus of questions about patient data in electronic health records (EHRs). The goal is provide the training data necessary for semantic parsers to automatically convert EHR questions into a structured query. A layered annotation strategy is used which mirrors a typical natural language processing (NLP) pipeline. First, questions are syntactically analyzed to identify multi-part questions. Second, medical concepts are recognized and normalized to a clinical ontology. Finally, logical forms are created using a lambda calculus representation. We use a corpus of 446 questions asking for patient-specific information. From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms. We further present detailed characteristics of the corpus, including inter-annotator agreement results, and describe the challenges automatic NLP systems will face on this task.

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A Semantically Compositional Annotation Scheme for Time Normalization
Steven Bethard | Jonathan Parker

We present a new annotation scheme for normalizing time expressions, such as “three days ago”, to computer-readable forms, such as 2016-03-07. The annotation scheme addresses several weaknesses of the existing TimeML standard, allowing the representation of time expressions that align to more than one calendar unit (e.g., “the past three summers”), that are defined relative to events (e.g., “three weeks postoperative”), and that are unions or intersections of smaller time expressions (e.g., “Tuesdays and Thursdays”). It achieves this by modeling time expression interpretation as the semantic composition of temporal operators like UNION, NEXT, and AFTER. We have applied the annotation scheme to 34 documents so far, producing 1104 annotations, and achieving inter-annotator agreement of 0.821.

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PROMETHEUS: A Corpus of Proverbs Annotated with Metaphors
Gözde Özbal | Carlo Strapparava | Serra Sinem Tekiroğlu

Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor identification or interpretation. Besides, a collection of proverbs fromvarious languages annotated with metaphors would also be essential for social scientists to explore the cultural differences betweenthose languages. In this paper, we introduce PROMETHEUS, a dataset consisting of English proverbs and their equivalents in Italian.In addition to the word-level metaphor annotations for each proverb, PROMETHEUS contains other types of information such as the metaphoricity degree of the overall proverb, its meaning, the century that it was first recorded in and a pair of subjective questions responded by the annotators. To the best of our knowledge, this is the first multi-lingual and open-domain corpus of proverbs annotated with word-level metaphors.

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Corpus Annotation within the French FrameNet: a Domain-by-domain Methodology
Marianne Djemaa | Marie Candito | Philippe Muller | Laure Vieu

This paper reports on the development of a French FrameNet, within the ASFALDA project. While the first phase of the project focused on the development of a French set of frames and corresponding lexicon (Candito et al., 2014), this paper concentrates on the subsequent corpus annotation phase, which focused on four notional domains (commercial transactions, cognitive stances, causality and verbal communication). Given full coverage is not reachable for a relatively “new” FrameNet project, we advocate that focusing on specific notional domains allowed us to obtain full lexical coverage for the frames of these domains, while partially reflecting word sense ambiguities. Furthermore, as frames and roles were annotated on two French Treebanks (the French Treebank (Abeillé and Barrier, 2004) and the Sequoia Treebank (Candito and Seddah, 2012), we were able to extract a syntactico-semantic lexicon from the annotated frames. In the resource’s current status, there are 98 frames, 662 frame evoking words, 872 senses, and about 13000 annotated frames, with their semantic roles assigned to portions of text. The French FrameNet is freely available at alpage.inria.fr/asfalda.

This paper reports a critical analysis of the ISO TimeML standard, in the light of several experiences of temporal annotation that were conducted on spoken French. It shows that the norm suffers from weaknesses that should be corrected to fit a larger variety of needs inNLP and in corpus linguistics. We present our proposition of some improvements of the norm before it will be revised by the ISO Committee in 2017. These modifications concern mainly (1) Enrichments of well identified features of the norm: temporal function of TIMEX time expressions, additional types for TLINK temporal relations; (2) Deeper modifications concerning the units or features annotated: clarification between time and tense for EVENT units, coherence of representation between temporal signals (the SIGNAL unit) and TIMEX modifiers (the MOD feature); (3) A recommendation to perform temporal annotation on top of a syntactic (rather than lexical) layer (temporal annotation on a treebank).

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A General Framework for the Annotation of Causality Based on FrameNet
Laure Vieu | Philippe Muller | Marie Candito | Marianne Djemaa

We present here a general set of semantic frames to annotate causal expressions, with a rich lexicon in French and an annotated corpus of about 5000 instances of causal lexical items with their corresponding semantic frames. The aim of our project is to have both the largest possible coverage of causal phenomena in French, across all parts of speech, and have it linked to a general semantic framework such as FN, to benefit in particular from the relations between other semantic frames, e.g., temporal ones or intentional ones, and the underlying upper lexical ontology that enable some forms of reasoning. This is part of the larger ASFALDA French FrameNet project, which focuses on a few different notional domains which are interesting in their own right (Djemma et al., 2016), including cognitive positions and communication frames. In the process of building the French lexicon and preparing the annotation of the corpus, we had to remodel some of the frames proposed in FN based on English data, with hopefully more precise frame definitions to facilitate human annotation. This includes semantic clarifications of frames and frame elements, redundancy elimination, and added coverage. The result is arguably a significant improvement of the treatment of causality in FN itself.

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Annotating Temporally-Anchored Spatial Knowledge on Top of OntoNotes Semantic Roles
Alakananda Vempala | Eduardo Blanco

This paper presents a two-step methodology to annotate spatial knowledge on top of OntoNotes semantic roles. First, we manipulate semantic roles to automatically generate potential additional spatial knowledge. Second, we crowdsource annotations with Amazon Mechanical Turk to either validate or discard the potential additional spatial knowledge. The resulting annotations indicate whether entities are or are not located somewhere with a degree of certainty, and temporally anchor this spatial information. Crowdsourcing experiments show that the additional spatial knowledge is ubiquitous and intuitive to humans, and experimental results show that it can be inferred automatically using standard supervised machine learning techniques.

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SpaceRef: A corpus of street-level geographic descriptions
Jana Götze | Johan Boye

This article describes SPACEREF, a corpus of street-level geographic descriptions. Pedestrians are walking a route in a (real) urban environment, describing their actions. Their position is automatically logged, their speech is manually transcribed, and their references to objects are manually annotated with respect to a crowdsourced geographic database. We describe how the data was collected and annotated, and how it has been used in the context of creating resources for an automatic pedestrian navigation system.

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Persian Proposition Bank
Azadeh Mirzaei | Amirsaeid Moloodi

This paper describes the procedure of semantic role labeling and the development of the first manually annotated Persian Proposition Bank (PerPB) which added a layer of predicate-argument information to the syntactic structures of Persian Dependency Treebank (known as PerDT). Through the process of annotating, the annotators could see the syntactic information of all the sentences and so they annotated 29982 sentences with more than 9200 unique verbs. In the annotation procedure, the direct syntactic dependents of the verbs were the first candidates for being annotated. So we did not annotate the other indirect dependents unless their phrasal heads were propositional and had their own arguments or adjuncts. Hence besides the semantic role labeling of verbs, the argument structure of 1300 unique propositional nouns and 300 unique propositional adjectives were annotated in the sentences, too. The accuracy of annotation process was measured by double annotation of the data at two separate stages and finally the data was prepared in the CoNLL dependency format.

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Typed Entity and Relation Annotation on Computer Science Papers
Yuka Tateisi | Tomoko Ohta | Sampo Pyysalo | Yusuke Miyao | Akiko Aizawa

We describe our ongoing effort to establish an annotation scheme for describing the semantic structures of research articles in the computer science domain, with the intended use of developing search systems that can refine their results by the roles of the entities denoted by the query keys. In our scheme, mentions of entities are annotated with ontology-based types, and the roles of the entities are annotated as relations with other entities described in the text. So far, we have annotated 400 abstracts from the ACL anthology and the ACM digital library. In this paper, the scheme and the annotated dataset are described, along with the problems found in the course of annotation. We also show the results of automatic annotation and evaluate the corpus in a practical setting in application to topic extraction.

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Enriching TimeBank: Towards a more precise annotation of temporal relations in a text
Volker Gast | Lennart Bierkandt | Stephan Druskat | Christoph Rzymski

We propose a way of enriching the TimeML annotations of TimeBank by adding information about the Topic Time in terms of Klein (1994). The annotations are partly automatic, partly inferential and partly manual. The corpus was converted into the native format of the annotation software GraphAnno and POS-tagged using the Stanford bidirectional dependency network tagger. On top of each finite verb, a FIN-node with tense information was created, and on top of any FIN-node, a TOPICTIME-node, in accordance with Klein’s (1994) treatment of finiteness as the linguistic correlate of the Topic Time. Each TOPICTIME-node is linked to a MAKEINSTANCE-node representing an (instantiated) event in TimeML (Pustejovsky et al. 2005), the markup language used for the annotation of TimeBank. For such links we introduce a new category, ELINK. ELINKs capture the relationship between the Topic Time (TT) and the Time of Situation (TSit) and have an aspectual interpretation in Klein’s (1994) theory. In addition to these automatic and inferential annotations, some TLINKs were added manually. Using an example from the corpus, we show that the inclusion of the Topic Time in the annotations allows for a richer representation of the temporal structure than does TimeML. A way of representing this structure in a diagrammatic form similar to the T-Box format (Verhagen, 2007) is proposed.

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How Diachronic Text Corpora Affect Context based Retrieval of OOV Proper Names for Audio News
Imran Sheikh | Irina Illina | Dominique Fohr

Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus. In this paper we investigate how the choice of documents for the diachronic text corpora affects the retrieval of OOV Proper Names (PNs) relevant to an audio document. We first present our diachronic French broadcast news datasets, which highlight the motivation of our study on OOV PNs. Then the effect of using diachronic text data from different sources and a different time span is analysed. With OOV PN retrieval experiments on French broadcast news videos, we conclude that a diachronic corpus with text from different sources leads to better retrieval performance than one relying on text from single source or from a longer time span.

This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model. This is a part of an effort in building a STT system to aid dyslexic students who have cognitive deficiency in writing skills but have no problem expressing their ideas through speech. For Cantonese speech recognition, the basic unit of acoustic models can either be the conventional Initial-Final (IF) syllables, or the Onset-Nucleus-Coda (ONC) syllables where finals are further split into nucleus and coda to reflect the intra-syllable variations in Cantonese. By using the Kaldi toolkit, our system is trained using the stochastic gradient descent optimization model with the aid of GPUs for the hybrid Deep Neural Network and Hidden Markov Model (DNN-HMM) with and without I-vector based speaker adaptive training technique. The input features of the same Gaussian Mixture Model with speaker adaptive training (GMM-SAT) to DNN are used in all cases. Experiments show that the ONC-based syllable acoustic modeling with I-vector based DNN-HMM achieves the best performance with the word error rate (WER) of 9.66% and the real time factor (RTF) of 1.38812.

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Collecting Resources in Sub-Saharan African Languages for Automatic Speech Recognition: a Case Study of Wolof
Elodie Gauthier | Laurent Besacier | Sylvie Voisin | Michael Melese | Uriel Pascal Elingui

This article presents the data collected and ASR systems developped for 4 sub-saharan african languages (Swahili, Hausa, Amharic and Wolof). To illustrate our methodology, the focus is made on Wolof (a very under-resourced language) for which we designed the first ASR system ever built in this language. All data and scripts are available online on our github repository.

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SCALE: A Scalable Language Engineering Toolkit
Joris Pelemans | Lyan Verwimp | Kris Demuynck | Hugo Van hamme | Patrick Wambacq

In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models. The first extension is a novel technique to model compound words called Semantic Head Mapping (SHM). The second extension, Bag-of-Words Language Modeling (BagLM), bundles popular models such as Latent Semantic Analysis and Continuous Skip-grams. Both extensions scale to large data and allow the integration into first-pass ASR decoding. The toolkit is open source, includes working examples and can be found on http://github.com/jorispelemans/scale.

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Combining Manual and Automatic Prosodic Annotation for Expressive Speech Synthesis
Sandrine Brognaux | Thomas François | Marco Saerens

Text-to-speech has long been centered on the production of an intelligible message of good quality. More recently, interest has shifted to the generation of more natural and expressive speech. A major issue of existing approaches is that they usually rely on a manual annotation in expressive styles, which tends to be rather subjective. A typical related issue is that the annotation is strongly influenced ― and possibly biased ― by the semantic content of the text (e.g. a shot or a fault may incite the annotator to tag that sequence as expressing a high degree of excitation, independently of its acoustic realization). This paper investigates the assumption that human annotation of basketball commentaries in excitation levels can be automatically improved on the basis of acoustic features. It presents two techniques for label correction exploiting a Gaussian mixture and a proportional-odds logistic regression. The automatically re-annotated corpus is then used to train HMM-based expressive speech synthesizers, the performance of which is assessed through subjective evaluations. The results indicate that the automatic correction of the annotation with Gaussian mixture helps to synthesize more contrasted excitation levels, while preserving naturalness.

In 2012 the Bavarian Archive for Speech Signals started providing some of its tools from the field of spoken language in the form of Software as a Service (SaaS). This means users access the processing functionality over a web browser and therefore do not have to install complex software packages on a local computer. Amongst others, these tools include segmentation & labeling, grapheme-to-phoneme conversion, text alignment, syllabification and metadata generation, where all but the last are available for a variety of languages. Since its creation the number of available services and the web interface have changed considerably. We give an overview and a detailed description of the system architecture, the available web services and their functionality. Furthermore, we show how the number of files processed over the system developed in the last four years.

This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web. It was developed with the aim of facilitating the usage of the modules, without the need to know about software dependencies and specific configurations. Apart from being accessed by a web-browser, the platform also provides a REST API for easy integration with other applications. The platform is flexible, scalable, provides authentication for access restrictions, and was developed taking into consideration the time and effort of providing new services. The platform is still being improved, but it already integrates a considerable number of audio and text processing modules, including: Automatic transcription, speech disfluency classification, emotion detection, dialog act recognition, age and gender classification, non-nativeness detection, hyper-articulation detection, dialog act recognition, and two external modules for feature extraction and DTMF detection. This paper describes the SPA architecture, presents the already integrated modules, and provides a detailed description for the ones most recently integrated.

The “Corpus Oral Informatizado da Lingua Galega (CORILGA)” project aims at building a corpus of oral language for Galician, primarily designed to study the linguistic variation and change. This project is currently under development and it is periodically enriched with new contributions. The long-term goal is that all the speech recordings will be enriched with phonetic, syllabic, morphosyntactic, lexical and sentence ELAN-complaint annotations. A way to speed up the process of annotation is to use automatic speech-recognition-based tools tailored to the application. Therefore, CORILGA repository has been enhanced with an automatic alignment tool, available to the administrator of the repository, that aligns speech with an orthographic transcription. In the event that no transcription, or just a partial one, were available, a speech recognizer for Galician is used to generate word and phonetic segmentations. These recognized outputs may contain errors that will have to be manually corrected by the administrator. For assisting this task, the tool also provides an ELAN tier with the confidence measure of each recognized word. In this paper, after the description of the main facts of the CORILGA corpus, the speech alignment and recognition tools are described. Both have been developed using the Kaldi toolkit.

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A Corpus of Argument Networks: Using Graph Properties to Analyse Divisive Issues
Barbara Konat | John Lawrence | Joonsuk Park | Katarzyna Budzynska | Chris Reed

Governments are increasingly utilising online platforms in order to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. This creates a demand for tools and technologies which will enable governments to quickly and thoroughly digest the points being made and to respond accordingly. By determining the argumentative and dialogical structures contained within a debate, we are able to determine the issues which are divisive and those which attract agreement. This paper proposes a method of graph-based analytics which uses properties of graphs representing networks of arguments pro- & con- in order to automatically analyse issues which divide citizens about new regulations. By future application of the most recent advances in argument mining, the results reported here will have a chance to scale up to enable sense-making of the vast amount of feedback received from citizens on directions that policy should take.

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metaTED: a Corpus of Metadiscourse for Spoken Language
Rui Correia | Nuno Mamede | Jorge Baptista | Maxine Eskenazi

This paper describes metaTED ― a freely available corpus of metadiscursive acts in spoken language collected via crowdsourcing. Metadiscursive acts were annotated on a set of 180 randomly chosen TED talks in English, spanning over different speakers and topics. The taxonomy used for annotation is composed of 16 categories, adapted from Adel(2010). This adaptation takes into account both the material to annotate and the setting in which the annotation task is performed. The crowdsourcing setup is described, including considerations regarding training and quality control. The collected data is evaluated in terms of quantity of occurrences, inter-annotator agreement, and annotation related measures (such as average time on task and self-reported confidence). Results show different levels of agreement among metadiscourse acts (α ∈ [0.15; 0.49]). To further assess the collected material, a subset of the annotations was submitted to expert appreciation, who validated which of the marked occurrences truly correspond to instances of the metadiscursive act at hand. Similarly to what happened with the crowd, experts revealed different levels of agreement between categories (α ∈ [0.18; 0.72]). The paper concludes with a discussion on the applicability of metaTED with respect to each of the 16 categories of metadiscourse.

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PARC 3.0: A Corpus of Attribution Relations
Silvia Pareti

Quotation and opinion extraction, discourse and factuality have all partly addressed the annotation and identification of Attribution Relations. However, disjoint efforts have provided a partial and partly inaccurate picture of attribution and generated small or incomplete resources, thus limiting the applicability of machine learning approaches. This paper presents PARC 3.0, a large corpus fully annotated with Attribution Relations (ARs). The annotation scheme was tested with an inter-annotator agreement study showing satisfactory results for the identification of ARs and high agreement on the selection of the text spans corresponding to its constitutive elements: source, cue and content. The corpus, which comprises around 20k ARs, was used to investigate the range of structures that can express attribution. The results show a complex and varied relation of which the literature has addressed only a portion. PARC 3.0 is available for research use and can be used in a range of different studies to analyse attribution and validate assumptions as well as to develop supervised attribution extraction models.

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Improving the Annotation of Sentence Specificity
Junyi Jessy Li | Bridget O’Daniel | Yi Wu | Wenli Zhao | Ani Nenkova

We introduce improved guidelines for annotation of sentence specificity, addressing the issues encountered in prior work. Our annotation provides judgements of sentences in context. Rather than binary judgements, we introduce a specificity scale which accommodates nuanced judgements. Our augmented annotation procedure also allows us to define where in the discourse context the lack of specificity can be resolved. In addition, the cause of the underspecification is annotated in the form of free text questions. We present results from a pilot annotation with this new scheme and demonstrate good inter-annotator agreement. We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context. We find that missing details that are not resolved in the the prior context are more likely to trigger questions about the reason behind events, “why” and “how”. Our data is accessible at http://www.cis.upenn.edu/~nlp/corpora/lrec16spec.html

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Focus Annotation of Task-based Data: A Comparison of Expert and Crowd-Sourced Annotation in a Reading Comprehension Corpus
Kordula De Kuthy | Ramon Ziai | Detmar Meurers

While the formal pragmatic concepts in information structure, such as the focus of an utterance, are precisely defined in theoretical linguistics and potentially very useful in conceptual and practical terms, it has turned out to be difficult to reliably annotate such notions in corpus data. We present a large-scale focus annotation effort designed to overcome this problem. Our annotation study is based on the tasked-based corpus CREG, which consists of answers to explicitly given reading comprehension questions. We compare focus annotation by trained annotators with a crowd-sourcing setup making use of untrained native speakers. Given the task context and an annotation process incrementally making the question form and answer type explicit, the trained annotators reach substantial agreement for focus annotation. Interestingly, the crowd-sourcing setup also supports high-quality annotation ― for specific subtypes of data. Finally, we turn to the question whether the relevance of focus annotation can be extrinsically evaluated. We show that automatic short-answer assessment significantly improves for focus annotated data. The focus annotated CREG corpus is freely available and constitutes the largest such resource for German.

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Homing in on Twitter Users: Evaluating an Enhanced Geoparser for User Profile Locations
Beatrice Alex | Clare Llewellyn | Claire Grover | Jon Oberlander | Richard Tobin

Twitter-related studies often need to geo-locate Tweets or Twitter users, identifying their real-world geographic locations. As tweet-level geotagging remains rare, most prior work exploited tweet content, timezone and network information to inform geolocation, or else relied on off-the-shelf tools to geolocate users from location information in their user profiles. However, such user location metadata is not consistently structured, causing such tools to fail regularly, especially if a string contains multiple locations, or if locations are very fine-grained. We argue that user profile location (UPL) and tweet location need to be treated as distinct types of information from which differing inferences can be drawn. Here, we apply geoparsing to UPLs, and demonstrate how task performance can be improved by adapting our Edinburgh Geoparser, which was originally developed for processing English text. We present a detailed evaluation method and results, including inter-coder agreement. We demonstrate that the optimised geoparser can effectively extract and geo-reference multiple locations at different levels of granularity with an F1-score of around 0.90. We also illustrate how geoparsed UPLs can be exploited for international information trade studies and country-level sentiment analysis.

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A Dataset for Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry

We can often detect from a person’s utterances whether he/she is in favor of or against a given target entity (a product, topic, another person, etc.). Here for the first time we present a dataset of tweets annotated for whether the tweeter is in favor of or against pre-chosen targets of interest―their stance. The targets of interest may or may not be referred to in the tweets, and they may or may not be the target of opinion in the tweets. The data pertains to six targets of interest commonly known and debated in the United States. Apart from stance, the tweets are also annotated for whether the target of interest is the target of opinion in the tweet. The annotations were performed by crowdsourcing. Several techniques were employed to encourage high-quality annotations (for example, providing clear and simple instructions) and to identify and discard poor annotations (for example, using a small set of check questions annotated by the authors). This Stance Dataset, which was subsequently also annotated for sentiment, can be used to better understand the relationship between stance, sentiment, entity relationships, and textual inference.

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Emotion Analysis on Twitter: The Hidden Challenge
Luca Dini | André Bittar

In this paper, we present an experiment to detect emotions in tweets. Unlike much previous research, we draw the important distinction between the tasks of emotion detection in a closed world assumption (i.e. every tweet is emotional) and the complicated task of identifying emotional versus non-emotional tweets. Given an apparent lack of appropriately annotated data, we created two corpora for these tasks. We describe two systems, one symbolic and one based on machine learning, which we evaluated on our datasets. Our evaluation shows that a machine learning classifier performs best on emotion detection, while a symbolic approach is better for identifying relevant (i.e. emotional) tweets.

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Crowdsourcing Salient Information from News and Tweets
Oana Inel | Tommaso Caselli | Lora Aroyo

The increasing streams of information pose challenges to both humans and machines. On the one hand, humans need to identify relevant information and consume only the information that lies at their interests. On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews. We consider events as prime factors to query for information and generate meaningful context. The focus of this paper is to acquire empirical insights for identifying salience features in tweets and news about a target event, i.e., the event of “whaling”. We first derive a methodology to identify such features by building up a knowledge space of the event enriched with relevant phrases, sentiments and ranked by their novelty. We applied this methodology on tweets and we have performed preliminary work towards adapting it to news articles. Our results show that crowdsourcing text relevance, sentiments and novelty (1) can be a main step in identifying salient information, and (2) provides a deeper and more precise understanding of the data at hand compared to state-of-the-art approaches.

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What does this Emoji Mean? A Vector Space Skip-Gram Model for Twitter Emojis
Francesco Barbieri | Francesco Ronzano | Horacio Saggion

Emojis allow us to describe objects, situations and even feelings with small images, providing a visual and quick way to communicate. In this paper, we analyse emojis used in Twitter with distributional semantic models. We retrieve 10 millions tweets posted by USA users, and we build several skip gram word embedding models by mapping in the same vectorial space both words and emojis. We test our models with semantic similarity experiments, comparing the output of our models with human assessment. We also carry out an exhaustive qualitative evaluation, showing interesting results.

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Crossmodal Network-Based Distributional Semantic Models
Elias Iosif | Alexandros Potamianos

Despite the recent success of distributional semantic models (DSMs) in various semantic tasks they remain disconnected with real-world perceptual cues since they typically rely on linguistic features. Text data constitute the dominant source of features for the majority of such models, although there is evidence from cognitive science that cues from other modalities contribute to the acquisition and representation of semantic knowledge. In this work, we propose the crossmodal extension of a two-tier text-based model, where semantic representations are encoded in the first layer, while the second layer is used for computing similarity between words. We exploit text- and image-derived features for performing computations at each layer, as well as various approaches for their crossmodal fusion. It is shown that the crossmodal model performs better (from 0.68 to 0.71 correlation coefficient) than the unimodal one for the task of similarity computation between words.

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Comprehensive and Consistent PropBank Light Verb Annotation
Claire Bonial | Martha Palmer

Recent efforts have focused on expanding the annotation coverage of PropBank from verb relations to adjective and noun relations, as well as light verb constructions (e.g., make an offer, take a bath). While each new relation type has presented unique annotation challenges, ensuring consistent and comprehensive annotation of light verb constructions has proved particularly challenging, given that light verb constructions are semi-productive, difficult to define, and there are often borderline cases. This research describes the iterative process of developing PropBank annotation guidelines for light verb constructions, the current guidelines, and a comparison to related resources.

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Inconsistency Detection in Semantic Annotation
Nora Hollenstein | Nathan Schneider | Bonnie Webber

Inconsistencies are part of any manually annotated corpus. Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data. Past research has focused mainly on detecting inconsistency in syntactic annotation. This work explores new approaches to detecting inconsistency in semantic annotation. Two ranking methods are presented in this paper: a discrepancy ranking and an entropy ranking. Those methods are then tested and evaluated on multiple corpora annotated with multiword expressions and supersense labels. The results show considerable improvements in detecting inconsistency candidates over a random baseline. Possible applications of methods for inconsistency detection are improving the annotation procedure as well as the guidelines and correcting errors in completed annotations.

We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP~2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.

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Event Coreference Resolution with Multi-Pass Sieves
Jing Lu | Vincent Ng

Multi-pass sieve approaches have been successfully applied to entity coreference resolution and many other tasks in natural language processing (NLP), owing in part to the ease of designing high-precision rules for these tasks. However, the same is not true for event coreference resolution: typically lying towards the end of the standard information extraction pipeline, an event coreference resolver assumes as input the noisy outputs of its upstream components such as the trigger identification component and the entity coreference resolution component. The difficulty in designing high-precision rules makes it challenging to successfully apply a multi-pass sieve approach to event coreference resolution. In this paper, we investigate this challenge, proposing the first multi-pass sieve approach to event coreference resolution. When evaluated on the version of the KBP 2015 corpus available to the participants of EN Task 2 (Event Nugget Detection and Coreference), our approach achieves an Avg F-score of 40.32%, outperforming the best participating system by 0.67% in Avg F-score.

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Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, ASR
Malgorzata Ćavar | Damir Ćavar | Hilaria Cruz

This project approaches the problem of language documentation and revitalization from a rather untraditional angle. To improve and facilitate language documentation of endangered languages, we attempt to use corpus linguistic methods and speech and language technologies to reduce the time needed for transcription and annotation of audio and video language recordings. The paper demonstrates this approach on the example of the endangered and seriously under-resourced variety of Eastern Chatino (CTP). We show how initial speech corpora can be created that can facilitate the development of speech and language technologies for under-resourced languages by utilizing Forced Alignment tools to time align transcriptions. Time-aligned transcriptions can be used to train speech corpora and utilize automatic speech recognition tools for the transcription and annotation of untranscribed data. Speech technologies can be used to reduce the time and effort necessary for transcription and annotation of large collections of audio and video recordings in digital language archives, addressing the transcription bottleneck problem that most language archives and many under-documented languages are confronted with. This approach can increase the availability of language resources from low-resourced and endangered languages to speech and language technology research and development.

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The DIRHA Portuguese Corpus: A Comparison of Home Automation Command Detection and Recognition in Simulated and Real Data.
Miguel Matos | Alberto Abad | António Serralheiro

In this paper, we describe a new corpus -named DIRHA-L2F RealCorpus- composed of typical home automation speech interactions in European Portuguese that has been recorded by the INESC-ID’s Spoken Language Systems Laboratory (L2F) to support the activities of the Distant-speech Interaction for Robust Home Applications (DIRHA) EU-funded project. The corpus is a multi-microphone and multi-room database of real continuous audio sequences containing read phonetically rich sentences, read and spontaneous keyword activation sentences, and read and spontaneous home automation commands. The background noise conditions are controlled and randomly recreated with noises typically found in home environments. Experimental validation on this corpus is reported in comparison with the results obtained on a simulated corpus using a fully automated speech processing pipeline for two fundamental automatic speech recognition tasks of typical ‘always-listening’ home-automation scenarios: system activation and voice command recognition. Attending to results on both corpora, the presence of overlapping voice-like noise is shown as the main problem: simulated sequences contain concurrent speakers that result in general in a more challenging corpus, while real sequences performance drops drastically when TV or radio is on.

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Accuracy of Automatic Cross-Corpus Emotion Labeling for Conversational Speech Corpus Commonization
Hiroki Mori | Atsushi Nagaoka | Yoshiko Arimoto

There exists a major incompatibility in emotion labeling framework among emotional speech corpora, that is, category-based and dimension-based. Commonizing these requires inter-corpus emotion labeling according to both frameworks, but doing this by human annotators is too costly for most cases. This paper examines the possibility of automatic cross-corpus emotion labeling. In order to evaluate the effectiveness of the automatic labeling, a comprehensive emotion annotation for two conversational corpora, UUDB and OGVC, was performed. With a state-of-the-art machine learning technique, dimensional and categorical emotion estimation models were trained and tested against the two corpora. For the emotion dimension estimation, the automatic cross-corpus emotion labeling for the different corpus was effective for the dimensions of aroused-sleepy, dominant-submissive and interested-indifferent, showing only slight performance degradation against the result for the same corpus. On the other hand, the performance for the emotion category estimation was not sufficient.

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English-to-Japanese Translation vs. Dictation vs. Post-editing: Comparing Translation Modes in a Multilingual Setting
Michael Carl | Akiko Aizawa | Masaru Yamada

Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production. However, not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation post-editing. The transcription of the spoken data, keyboard logging and eye-tracking data were recorded with Translog-II, post-processed and integrated into the CRITT Translation Process Research-DB (TPR-DB), which is publicly available under a creative commons license. The paper presents the ENJA15 data as part of a large multilingual Chinese, Danish, German, Hindi and Spanish translation process data collection of more than 760 translation sessions. It compares the ENJA15 data with the other language pairs and reviews some of its particularities.

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Database of Mandarin Neighborhood Statistics
Karl Neergaard | Hongzhi Xu | Chu-Ren Huang

In the design of controlled experiments with language stimuli, researchers from psycholinguistic, neurolinguistic, and related fields, require language resources that isolate variables known to affect language processing. This article describes a freely available database that provides word level statistics for words and nonwords of Mandarin, Chinese. The featured lexical statistics include subtitle corpus frequency, phonological neighborhood density, neighborhood frequency, and homophone density. The accompanying word descriptors include pinyin, ascii phonetic transcription (sampa), lexical tone, syllable structure, dominant PoS, and syllable, segment and pinyin lengths for each phonological word. It is designed for researchers particularly concerned with language processing of isolated words and made to accommodate multiple existing hypotheses concerning the structure of the Mandarin syllable. The database is divided into multiple files according to the desired search criteria: 1) the syllable segmentation schema used to calculate density measures, and 2) whether the search is for words or nonwords. The database is open to the research community at https://github.com/karlneergaard/Mandarin-Neighborhood-Statistics.

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TEITOK: Text-Faithful Annotated Corpora
Maarten Janssen

TEITOK is a web-based framework for corpus creation, annotation, and distribution, that combines textual and linguistic annotation within a single TEI based XML document. TEITOK provides several built-in NLP tools to automatically (pre)process texts, and is highly customizable. It features multiple orthographic transcription layers, and a wide range of user-defined token-based annotations. For searching, TEITOK interfaces with a local CQP server. TEITOK can handle various types of additional resources including Facsimile images and linked audio files, making it possible to have a combined written/spoken corpus. It also has additional modules for PSDX syntactic annotation and several types of stand-off annotation.

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Extracting Interlinear Glossed Text from LaTeX Documents
Mathias Schenner | Sebastian Nordhoff

We present texigt, a command-line tool for the extraction of structured linguistic data from LaTeX source documents, and a language resource that has been generated using this tool: a corpus of interlinear glossed text (IGT) extracted from open access books published by Language Science Press. Extracted examples are represented in a simple XML format that is easy to process and can be used to validate certain aspects of interlinear glossed text. The main challenge involved is the parsing of TeX and LaTeX documents. We review why this task is impossible in general and how the texhs Haskell library uses a layered architecture and selective early evaluation (expansion) during lexing and parsing in order to provide access to structured representations of LaTeX documents at several levels. In particular, its parsing modules generate an abstract syntax tree for LaTeX documents after expansion of all user-defined macros and lexer-level commands that serves as an ideal interface for the extraction of interlinear glossed text by texigt. This architecture can easily be adapted to extract other types of linguistic data structures from LaTeX source documents.

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Interoperability of Annotation Schemes: Using the Pepper Framework to Display AWA Documents in the ANNIS Interface
Talvany Carlotto | Zuhaitz Beloki | Xabier Artola | Aitor Soroa

Natural language processing applications are frequently integrated to solve complex linguistic problems, but the lack of interoperability between these tools tends to be one of the main issues found in that process. That is often caused by the different linguistic formats used across the applications, which leads to attempts to both establish standard formats to represent linguistic information and to create conversion tools to facilitate this integration. Pepper is an example of the latter, as a framework that helps the conversion between different linguistic annotation formats. In this paper, we describe the use of Pepper to convert a corpus linguistically annotated by the annotation scheme AWA into the relANNIS format, with the ultimate goal of interacting with AWA documents through the ANNIS interface. The experiment converted 40 megabytes of AWA documents, allowed their use on the ANNIS interface, and involved making architectural decisions during the mapping from AWA into relANNIS using Pepper. The main issues faced during this process were due to technical issues mainly caused by the integration of the different systems and projects, namely AWA, Pepper and ANNIS.

Text preprocessing is an important and necessary task for all NLP applications. A simple variation in any preprocessing step may drastically affect the final results. Moreover replicability and comparability, as much as feasible, is one of the goals of our scientific enterprise, thus building systems that can ensure the consistency in our various pipelines would contribute significantly to our goals. The problem has become quite pronounced with the abundance of NLP tools becoming more and more available yet with different levels of specifications. In this paper, we present a dynamic unified preprocessing framework and tool, SPLIT, that is highly configurable based on user requirements which serves as a preprocessing tool for several tools at once. SPLIT aims to standardize the implementations of the most important preprocessing steps by allowing for a unified API that could be exchanged across different researchers to ensure complete transparency in replication. The user is able to select the required preprocessing tasks among a long list of preprocessing steps. The user is also able to specify the order of execution which in turn affects the final preprocessing output.

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ArchiMob - A Corpus of Spoken Swiss German
Tanja Samardžić | Yves Scherrer | Elvira Glaser

Swiss dialects of German are, unlike most dialects of well standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety rarely recorded and that it is subject to considerable regional variation. This paper presents a freely available general-purpose corpus of spoken Swiss German suitable for linguistic research, but also for training automatic tools. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first describe how the documents were transcribed, segmented and aligned with the sound source, and how inconsistent transcriptions were unified through an additional normalisation layer. We then present a bootstrapping approach to automatic normalisation using different machine-translation-inspired methods. Furthermore, we evaluate the performance of part-of-speech taggers on our data and show how the same bootstrapping approach improves part-of-speech tagging by 10% over four rounds. Finally, we present the modalities of access of the corpus as well as the data format.

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Word Segmentation for Akkadian Cuneiform
Timo Homburg | Christian Chiarcos

We present experiments on word segmentation for Akkadian cuneiform, an ancient writing system and a language used for about 3 millennia in the ancient Near East. To our best knowledge, this is the first study of this kind applied to either the Akkadian language or the cuneiform writing system. As a logosyllabic writing system, cuneiform structurally resembles Eastern Asian writing systems, so, we employ word segmentation algorithms originally developed for Chinese and Japanese. We describe results of rule-based algorithms, dictionary-based algorithms, statistical and machine learning approaches. Our results may indicate possible promising steps in cuneiform word segmentation that can create and improve natural language processing in this area.

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Controlled Propagation of Concept Annotations in Textual Corpora
Cyril Grouin

In this paper, we presented the annotation propagation tool we designed to be used in conjunction with the BRAT rapid annotation tool. We designed two experiments to annotate a corpus of 60 files, first not using our tool, second using our propagation tool. We evaluated the annotation time and the quality of annotations. We shown that using the annotation propagation tool reduces by 31.7% the time spent to annotate the corpus with a better quality of results.

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Graphical Annotation for Syntax-Semantics Mapping
Kôiti Hasida

A potential work item (PWI) for ISO standard (MAP) about linguistic annotation concerning syntax-semantics mapping is discussed. MAP is a framework for graphical linguistic annotation to specify a mapping (set of combinations) between possible syntactic and semantic structures of the annotated linguistic data. Just like a UML diagram, a MAP diagram is formal, in the sense that it accurately specifies such a mapping. MAP provides a diagrammatic sort of concrete syntax for linguistic annotation far easier to understand than textual concrete syntax such as in XML, so that it could better facilitate collaborations among people involved in research, standardization, and practical use of linguistic data. MAP deals with syntactic structures including dependencies, coordinations, ellipses, transsentential constructions, and so on. Semantic structures treated by MAP are argument structures, scopes, coreferences, anaphora, discourse relations, dialogue acts, and so forth. In order to simplify explicit annotations, MAP allows partial descriptions, and assumes a few general rules on correspondence between syntactic and semantic compositions.

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.

This paper introduces MADAD, a general-purpose annotation tool for Arabic text with focus on readability annotation. This tool will help in overcoming the problem of lack of Arabic readability training data by providing an online environment to collect readability assessments on various kinds of corpora. Also the tool supports a broad range of annotation tasks for various linguistic and semantic phenomena by allowing users to create their customized annotation schemes. MADAD is a web-based tool, accessible through any web browser; the main features that distinguish MADAD are its flexibility, portability, customizability and its bilingual interface (Arabic/English).

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Modeling Language Change in Historical Corpora: The Case of Portuguese
Marcos Zampieri | Shervin Malmasi | Mark Dras

This paper presents a number of experiments to model changes in a historical Portuguese corpus composed of literary texts for the purpose of temporal text classification. Algorithms were trained to classify texts with respect to their publication date taking into account lexical variation represented as word n-grams, and morphosyntactic variation represented by part-of-speech (POS) distribution. We report results of 99.8% accuracy using word unigram features with a Support Vector Machines classifier to predict the publication date of documents in time intervals of both one century and half a century. A feature analysis is performed to investigate the most informative features for this task and how they are linked to language change.

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“He Said She Said” ― a Male/Female Corpus of Polish
Filip Graliński | Łukasz Borchmann | Piotr Wierzchoń

Gender differences in language use have long been of interest in linguistics. The task of automatic gender attribution has been considered in computational linguistics as well. Most research of this type is done using (usually English) texts with authorship metadata. In this paper, we propose a new method of male/female corpus creation based on gender-specific first-person expressions. The method was applied on CommonCrawl Web corpus for Polish (language, in which gender-revealing first-person expressions are particularly frequent) to yield a large (780M words) and varied collection of men’s and women’s texts. The whole procedure for building the corpus and filtering out unwanted texts is described in the present paper. The quality check was done on a random sample of the corpus to make sure that the majority (84%) of texts are correctly attributed, natural texts. Some preliminary (socio)linguistic insights (websites and words frequently occurring in male/female fragments) are given as well.

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Cohere: A Toolkit for Local Coherence
Karin Sim Smith | Wilker Aziz | Lucia Specia

We describe COHERE, our coherence toolkit which incorporates various complementary models for capturing and measuring different aspects of text coherence. In addition to the traditional entity grid model (Lapata, 2005) and graph-based metric (Guinaudeau and Strube, 2013), we provide an implementation of a state-of-the-art syntax-based model (Louis and Nenkova, 2012), as well as an adaptation of this model which shows significant performance improvements in our experiments. We benchmark these models using the standard setting for text coherence: original documents and versions of the document with sentences in shuffled order.

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Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus
James Ravenscroft | Anika Oellrich | Shyamasree Saha | Maria Liakata

With the constant growth of the scientific literature, automated processes to enable access to its contents are increasingly in demand. Several functional discourse annotation schemes have been proposed to facilitate information extraction and summarisation from scientific articles, the most well known being argumentative zoning. Core Scientific concepts (CoreSC) is a three layered fine-grained annotation scheme providing content-based annotations at the sentence level and has been used to index, extract and summarise scientific publications in the biomedical literature. A previously developed CoreSC corpus on which existing automated tools have been trained contains a single annotation for each sentence. However, it is the case that more than one CoreSC concept can appear in the same sentence. Here, we present the Multi-CoreSC CRA corpus, a text corpus specific to the domain of cancer risk assessment (CRA), consisting of 50 full text papers, each of which contains sentences annotated with one or more CoreSCs. The full text papers have been annotated by three biology experts. We present several inter-annotator agreement measures appropriate for multi-label annotation assessment. Employing several inter-annotator agreement measures, we were able to identify the most reliable annotator and we built a harmonised consensus (gold standard) from the three different annotators, while also taking concept priority (as specified in the guidelines) into account. We also show that the new Multi-CoreSC CRA corpus allows us to improve performance in the recognition of CoreSCs. The updated guidelines, the multi-label CoreSC CRA corpus and other relevant, related materials are available at the time of publication at http://www.sapientaproject.com/.

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Detecting Expressions of Blame or Praise in Text
Udochukwu Orizu | Yulan He

The growth of social networking platforms has drawn a lot of attentions to the need for social computing. Social computing utilises human insights for computational tasks as well as design of systems that support social behaviours and interactions. One of the key aspects of social computing is the ability to attribute responsibility such as blame or praise to social events. This ability helps an intelligent entity account and understand other intelligent entities’ social behaviours, and enriches both the social functionalities and cognitive aspects of intelligent agents. In this paper, we present an approach with a model for blame and praise detection in text. We build our model based on various theories of blame and include in our model features used by humans determining judgment such as moral agent causality, foreknowledge, intentionality and coercion. An annotated corpus has been created for the task of blame and praise detection from text. The experimental results show that while our model gives similar results compared to supervised classifiers on classifying text as blame, praise or others, it outperforms supervised classifiers on more finer-grained classification of determining the direction of blame and praise, i.e., self-blame, blame-others, self-praise or praise-others, despite not using labelled training data.

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Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
Stéphan Tulkens | Chris Emmery | Walter Daelemans

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.

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SatiricLR: a Language Resource of Satirical News Articles
Alice Frain | Sander Wubben

In this paper we introduce the Satirical Language Resource: a dataset containing a balanced collection of satirical and non satirical news texts from various domains. This is the first dataset of this magnitude and scope in the domain of satire. We envision this dataset will facilitate studies on various aspects of of sat- ire in news articles. We test the viability of our data on the task of classification of satire.

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WIKIPARQ: A Tabulated Wikipedia Resource Using the Parquet Format
Marcus Klang | Pierre Nugues

Wikipedia has become one of the most popular resources in natural language processing and it is used in quantities of applications. However, Wikipedia requires a substantial pre-processing step before it can be used. For instance, its set of nonstandardized annotations, referred to as the wiki markup, is language-dependent and needs specific parsers from language to language, for English, French, Italian, etc. In addition, the intricacies of the different Wikipedia resources: main article text, categories, wikidata, infoboxes, scattered into the article document or in different files make it difficult to have global view of this outstanding resource. In this paper, we describe WikiParq, a unified format based on the Parquet standard to tabulate and package the Wikipedia corpora. In combination with Spark, a map-reduce computing framework, and the SQL query language, WikiParq makes it much easier to write database queries to extract specific information or subcorpora from Wikipedia, such as all the first paragraphs of the articles in French, or all the articles on persons in Spanish, or all the articles on persons that have versions in French, English, and Spanish. WikiParq is available in six language versions and is potentially extendible to all the languages of Wikipedia. The WikiParq files are downloadable as tarball archives from this location: http://semantica.cs.lth.se/wikiparq/.

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EN-ES-CS: An English-Spanish Code-Switching Twitter Corpus for Multilingual Sentiment Analysis
David Vilares | Miguel A. Alonso | Carlos Gómez-Rodríguez

Code-switching texts are those that contain terms in two or more different languages, and they appear increasingly often in social media. The aim of this paper is to provide a resource to the research community to evaluate the performance of sentiment classification techniques on this complex multilingual environment, proposing an English-Spanish corpus of tweets with code-switching (EN-ES-CS CORPUS). The tweets are labeled according to two well-known criteria used for this purpose: SentiStrength and a trinary scale (positive, neutral and negative categories). Preliminary work on the resource is already done, providing a set of baselines for the research community.

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SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines
Darina Benikova | Chris Biemann

Semantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use of contextuality of classic semantic relations beyond the boundary of one sentence. We present the SemRelData dataset that contains annotations of semantic relations between nominals in the context of one paragraph. To be able to analyse the universality of this context notion, the annotation was performed on a multi-lingual and multi-genre corpus. To evaluate the dataset, it is compared to large, manually created knowledge resources in the respective languages. The comparison shows that knowledge bases not only have coverage gaps; they also do not account for semantic relations that are manifested in particular contexts only, yet still play an important role for text cohesion.

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A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection
Jérémy Ferrero | Frédéric Agnès | Laurent Besacier | Didier Schwab

In this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present preexisting corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset.

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An Arabic-Moroccan Darija Code-Switched Corpus
Younes Samih | Wolfgang Maier

In this paper, we describe our effort in the development and annotation of a large scale corpus containing code-switched data. Until recently, very limited effort has been devoted to develop computational approaches or even basic linguistic resources to support research into the processing of Moroccan Darija.

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Standard Test Collection for English-Persian Cross-Lingual Word Sense Disambiguation
Navid Rekabsaz | Serwah Sabetghadam | Mihai Lupu | Linda Andersson | Allan Hanbury

In this paper, we address the shortage of evaluation benchmarks on Persian (Farsi) language by creating and making available a new benchmark for English to Persian Cross Lingual Word Sense Disambiguation (CL-WSD). In creating the benchmark, we follow the format of the SemEval 2013 CL-WSD task, such that the introduced tools of the task can also be applied on the benchmark. In fact, the new benchmark extends the SemEval-2013 CL-WSD task to Persian language.

In the recent years, Linked Data and Language Technology solutions gained popularity. Nevertheless, their coupling in real-world business is limited due to several issues. Existing products and services are developed for a particular domain, can be used only in combination with already integrated datasets or their language coverage is limited. In this paper, we present an innovative solution FREME - an open framework of e-Services for multilingual and semantic enrichment of digital content. The framework integrates six interoperable e-Services. We describe the core features of each e-Service and illustrate their usage in the context of four business cases: i) authoring and publishing; ii) translation and localisation; iii) cross-lingual access to data; and iv) personalised Web content recommendations. Business cases drive the design and development of the framework.

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Improving Bilingual Terminology Extraction from Comparable Corpora via Multiple Word-Space Models
Amir Hazem | Emmanuel Morin

There is a rich flora of word space models that have proven their efficiency in many different applications including information retrieval (Dumais, 1988), word sense disambiguation (Schutze, 1992), various semantic knowledge tests (Lund et al., 1995; Karlgren, 2001), and text categorization (Sahlgren, 2005). Based on the assumption that each model captures some aspects of word meanings and provides its own empirical evidence, we present in this paper a systematic exploration of the principal corpus-based word space models for bilingual terminology extraction from comparable corpora. We find that, once we have identified the best procedures, a very simple combination approach leads to significant improvements compared to individual models.

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MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP
Alexandre Bérard | Christophe Servan | Olivier Pietquin | Laurent Besacier

We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec’s features, paragraph vector (batch and online) and bivec for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.

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Creation of comparable corpora for English-Urdu, Arabic, Persian
Murad Abouammoh | Kashif Shah | Ahmet Aker

Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora. However, in the case of under-resourced languages or some specific domains, parallel corpora are not readily available. This leads to under-performing machine translation systems in those sparse data settings. To overcome the low availability of parallel resources the machine translation community has recognized the potential of using comparable resources as training data. However, most efforts have been related to European languages and less in middle-east languages. In this study, we report comparable corpora created from news articles for the pair English ―{Arabic, Persian, Urdu} languages. The data has been collected over a period of a year, entails Arabic, Persian and Urdu languages. Furthermore using the English as a pivot language, comparable corpora that involve more than one language can be created, e.g. English- Arabic - Persian, English - Arabic - Urdu, English ― Urdu - Persian, etc. Upon request the data can be provided for research purposes.

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A Corpus of Native, Non-native and Translated Texts
Sergiu Nisioi | Ella Rabinovich | Liviu P. Dinu | Shuly Wintner

We describe a monolingual English corpus of original and (human) translated texts, with an accurate annotation of speaker properties, including the original language of the utterances and the speaker’s country of origin. We thus obtain three sub-corpora of texts reflecting native English, non-native English, and English translated from a variety of European languages. This dataset will facilitate the investigation of similarities and differences between these kinds of sub-languages. Moreover, it will facilitate a unified comparative study of translations and language produced by (highly fluent) non-native speakers, two closely-related phenomena that have only been studied in isolation so far.

In an intercomprehension scenario, typically a native speaker of language L1 is confronted with output from an unknown, but related language L2. In this setting, the degree to which the receiver recognizes the unfamiliar words greatly determines communicative success. Despite exhibiting great string-level differences, cognates may be recognized very successfully if the receiver is aware of regular correspondences which allow to transform the unknown word into its familiar form. Modeling L1-L2 intercomprehension then requires the identification of all the regular correspondences between languages L1 and L2. We here present a set of linguistic orthographic correspondences manually compiled from comparative linguistics literature along with a set of statistically-inferred suggestions for correspondence rules. In order to do statistical inference, we followed the Minimum Description Length principle, which proposes to choose those rules which are most effective at describing the data. Our statistical model was able to reproduce most of our linguistic correspondences (88.5% for Czech-Polish and 75.7% for Bulgarian-Russian) and furthermore allowed to easily identify many more non-trivial correspondences which also cover aspects of morphology.

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Axolotl: a Web Accessible Parallel Corpus for Spanish-Nahuatl
Ximena Gutierrez-Vasques | Gerardo Sierra | Isaac Hernandez Pompa

This paper describes the project called Axolotl which comprises a Spanish-Nahuatl parallel corpus and its search interface. Spanish and Nahuatl are distant languages spoken in the same country. Due to the scarcity of digital resources, we describe the several problems that arose when compiling this corpus: most of our sources were non-digital books, we faced errors when digitizing the sources and there were difficulties in the sentence alignment process, just to mention some. The documents of the parallel corpus are not homogeneous, they were extracted from different sources, there is dialectal, diachronical, and orthographical variation. Additionally, we present a web search interface that allows to make queries through the whole parallel corpus, the system is capable to retrieve the parallel fragments that contain a word or phrase searched by a user in any of the languages. To our knowledge, this is the first Spanish-Nahuatl public available digital parallel corpus. We think that this resource can be useful to develop language technologies and linguistic studies for this language pair.

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A Turkish-German Code-Switching Corpus
Özlem Çetinoğlu

Bilingual communities often alternate between languages both in spoken and written communication. One such community, Germany residents of Turkish origin produce Turkish-German code-switching, by heavily mixing two languages at discourse, sentence, or word level. Code-switching in general, and Turkish-German code-switching in particular, has been studied for a long time from a linguistic perspective. Yet resources to study them from a more computational perspective are limited due to either small size or licence issues. In this work we contribute the solution of this problem with a corpus. We present a Turkish-German code-switching corpus which consists of 1029 tweets, with a majority of intra-sentential switches. We share different type of code-switching we have observed in our collection and describe our processing steps. The first step is data collection and filtering. This is followed by manual tokenisation and normalisation. And finally, we annotate data with word-level language identification information. The resulting corpus is available for research purposes.

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Introducing the LCC Metaphor Datasets
Michael Mohler | Mary Brunson | Bryan Rink | Marc Tomlinson

In this work, we present the Language Computer Corporation (LCC) annotated metaphor datasets, which represent the largest and most comprehensive resource for metaphor research to date. These datasets were produced over the course of three years by a staff of nine annotators working in four languages (English, Spanish, Russian, and Farsi). As part of these datasets, we provide (1) metaphoricity ratings for within-sentence word pairs on a four-point scale, (2) scored links to our repository of 114 source concept domains and 32 target concept domains, and (3) ratings for the affective polarity and intensity of each pair. Altogether, we provide 188,741 annotations in English (for 80,100 pairs), 159,915 annotations in Spanish (for 63,188 pairs), 99,740 annotations in Russian (for 44,632 pairs), and 137,186 annotations in Farsi (for 57,239 pairs). In addition, we are providing a large set of likely metaphors which have been independently extracted by our two state-of-the-art metaphor detection systems but which have not been analyzed by our team of annotators.

We present our effort to create a large Multi-Layered representational repository of Linguistic Code-Switched Arabic data. The process involves developing clear annotation standards and Guidelines, streamlining the annotation process, and implementing quality control measures. We used two main protocols for annotation: in-lab gold annotations and crowd sourcing annotations. We developed a web-based annotation tool to facilitate the management of the annotation process. The current version of the repository contains a total of 886,252 tokens that are tagged into one of sixteen code-switching tags. The data exhibits code switching between Modern Standard Arabic and Egyptian Dialectal Arabic representing three data genres: Tweets, commentaries, and discussion fora. The overall Inter-Annotator Agreement is 93.1%.

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Modelling a Parallel Corpus of French and French Belgian Sign Language
Laurence Meurant | Maxime Gobert | Anthony Cleve

The overarching objective underlying this research is to develop an online tool, based on a parallel corpus of French Belgian Sign Language (LSFB) and written Belgian French. This tool is aimed to assist various set of tasks related to the comparison of LSFB and French, to the benefit of general users as well as teachers in bilingual schools, translators and interpreters, as well as linguists. These tasks include (1) the comprehension of LSFB or French texts, (2) the production of LSFB or French texts, (3) the translation between LSFB and French in both directions and (4) the contrastive analysis of these languages. The first step of investigation aims at creating an unidirectional French-LSFB concordancer, able to align a one- or multiple-word expression from the French translated text with its corresponding expressions in the videotaped LSFB productions. We aim at testing the efficiency of this concordancer for the extraction of a dictionary of meanings in context. In this paper, we will present the modelling of the different data sources at our disposal and specifically the way they interact with one another.

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Building the Macedonian-Croatian Parallel Corpus
Ines Cebović | Marko Tadić

In this paper we present the newly created parallel corpus of two under-resourced languages, namely, Macedonian-Croatian Parallel Corpus (mk-hr_pcorp) that has been collected during 2015 at the Faculty of Humanities and Social Sciences, University of Zagreb. The mk-hr_pcorp is a unidirectional (mk→hr) parallel corpus composed of synchronic fictional prose texts received already in digital form with over 500 Kw in each language. The corpus was sentence segmented and provides 39,735 aligned sentences. The alignment was done automatically and then post-corrected manually. The alignments order was shuffled and this enabled the corpus to be available under CC-BY license through META-SHARE. However, this prevents the research in language units over the sentence level.

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Two Years of Aranea: Increasing Counts and Tuning the Pipeline
Vladimír Benko

The Aranea Project is targeted at creation of a family of Gigaword web-corpora for a dozen of languages that could be used for teaching language- and linguistics-related subjects at Slovak universities, as well as for research purposes in various areas of linguistics. All corpora are being built according to a standard methodology and using the same set of tools for processing and annotation, which ― together with their standard size and― makes them also a valuable resource for translators and contrastive studies. All our corpora are freely available either via a web interface or in a source form in an annotated vertical format.

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Quantitative Analysis of Gazes and Grounding Acts in L1 and L2 Conversations
Ichiro Umata | Koki Ijuin | Mitsuru Ishida | Moe Takeuchi | Seiichi Yamamoto

The listener’s gazing activities during utterances were analyzed in a face-to-face three-party conversation setting. The function of each utterance was categorized according to the Grounding Acts defined by Traum (Traum, 1994) so that gazes during utterances could be analyzed from the viewpoint of grounding in communication (Clark, 1996). Quantitative analysis showed that the listeners were gazing at the speakers more in the second language (L2) conversation than in the native language (L1) conversation during the utterances that added new pieces of information, suggesting that they are using visual information to compensate for their lack of linguistic proficiency in L2 conversation.

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Multi-language Speech Collection for NIST LRE
Karen Jones | Stephanie Strassel | Kevin Walker | David Graff | Jonathan Wright

The Multi-language Speech (MLS) Corpus supports NIST’s Language Recognition Evaluation series by providing new conversational telephone speech and broadcast narrowband data in 20 languages/dialects. The corpus was built with the intention of testing system performance in the matter of distinguishing closely related or confusable linguistic varieties, and careful manual auditing of collected data was an important aspect of this work. This paper lists the specific data requirements for the collection and provides both a commentary on the rationale for those requirements as well as an outline of the various steps taken to ensure all goals were met as specified. LDC conducted a large-scale recruitment effort involving the implementation of candidate assessment and interview techniques suitable for hiring a large contingent of telecommuting workers, and this recruitment effort is discussed in detail. We also describe the telephone and broadcast collection infrastructure and protocols, and provide details of the steps taken to pre-process collected data prior to auditing. Finally, annotation training, procedures and outcomes are presented in detail.

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FlexTag: A Highly Flexible PoS Tagging Framework
Torsten Zesch | Tobias Horsmann

We present FlexTag, a highly flexible PoS tagging framework. In contrast to monolithic implementations that can only be retrained but not adapted otherwise, FlexTag enables users to modify the feature space and the classification algorithm. Thus, FlexTag makes it easy to quickly develop custom-made taggers exactly fitting the research problem.

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New Inflectional Lexicons and Training Corpora for Improved Morphosyntactic Annotation of Croatian and Serbian
Nikola Ljubešić | Filip Klubička | Željko Agić | Ivo-Pavao Jazbec

In this paper we present newly developed inflectional lexcions and manually annotated corpora of Croatian and Serbian. We introduce hrLex and srLex - two freely available inflectional lexicons of Croatian and Serbian - and describe the process of building these lexicons, supported by supervised machine learning techniques for lemma and paradigm prediction. Furthermore, we introduce hr500k, a manually annotated corpus of Croatian, 500 thousand tokens in size. We showcase the three newly developed resources on the task of morphosyntactic annotation of both languages by using a recently developed CRF tagger. We achieve best results yet reported on the task for both languages, beating the HunPos baseline trained on the same datasets by a wide margin.

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TGermaCorp – A (Digital) Humanities Resource for (Computational) Linguistics
Andy Luecking | Armin Hoenen | Alexander Mehler

TGermaCorp is a German text corpus whose primary sources are collected from German literature texts which date from the sixteenth century to the present. The corpus is intended to represent its target language (German) in syntactic, lexical, stylistic and chronological diversity. For this purpose, it is hand-annotated on several linguistic layers, including POS, lemma, named entities, multiword expressions, clauses, sentences and paragraphs. In order to introduce TGermaCorp in comparison to more homogeneous corpora of contemporary everyday language, quantitative assessments of syntactic and lexical diversity are provided. In this respect, TGermaCorp contributes to establishing characterising features for resource descriptions, which is needed for keeping track of a meaningful comparison of the ever-growing number of natural language resources. The assessments confirm the special role of proper names, whose propagation in text may influence lexical and syntactic diversity measures in rather trivial ways. TGermaCorp will be made available via hucompute.org.

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The hunvec framework for NN-CRF-based sequential tagging
Katalin Pajkossy | Attila Zséder

In this work we present the open source hunvec framework for sequential tagging, built upon Theano and Pylearn2. The underlying statistical model, which connects linear CRF-s with neural networks, was used by Collobert and co-workers, and several other researchers. For demonstrating the flexibility of our tool, we describe a set of experiments on part-of-speech and named-entity-recognition tasks, using English and Hungarian datasets, where we modify both model and training parameters, and illustrate the usage of custom features. Model parameters we experiment with affect the vectorial word representations used by the model; we apply different word vector initializations, defined by Word2vec and GloVe embeddings and enrich the representation of words by vectors assigned trigram features. We extend training methods by using their regularized (l2 and dropout) version. When testing our framework on a Hungarian named entity corpus, we find that its performance reaches the best published results on this dataset, with no need for language-specific feature engineering. Our code is available at http://github.com/zseder/hunvec

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A Large Scale Corpus of Gulf Arabic
Salam Khalifa | Nizar Habash | Dana Abdulrahim | Sara Hassan

Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, however, lags behind in that respect. In this paper, we present the Gumar Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. We annotate the corpus for sub-dialect information at the document level. We also present results of a preliminary study in the morphological annotation of Gulf Arabic which includes developing guidelines for a conventional orthography. The text of the corpus is publicly browsable through a web interface we developed for it.

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UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing
Milan Straka | Jan Hajič | Jana Straková

Automatic natural language processing of large texts often presents recurring challenges in multiple languages: even for most advanced tasks, the texts are first processed by basic processing steps – from tokenization to parsing. We present an extremely simple-to-use tool consisting of one binary and one model (per language), which performs these tasks for multiple languages without the need for any other external data. UDPipe, a pipeline processing CoNLL-U-formatted files, performs tokenization, morphological analysis, part-of-speech tagging, lemmatization and dependency parsing for nearly all treebanks of Universal Dependencies 1.2 (namely, the whole pipeline is currently available for 32 out of 37 treebanks). In addition, the pipeline is easily trainable with training data in CoNLL-U format (and in some cases also with additional raw corpora) and requires minimal linguistic knowledge on the users’ part. The training code is also released.

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Exploiting Arabic Diacritization for High Quality Automatic Annotation
Nizar Habash | Anas Shahrour | Muhamed Al-Khalil

We present a novel technique for Arabic morphological annotation. The technique utilizes diacritization to produce morphological annotations of quality comparable to human annotators. Although Arabic text is generally written without diacritics, diacritization is already available for large corpora of Arabic text in several genres. Furthermore, diacritization can be generated at a low cost for new text as it does not require specialized training beyond what educated Arabic typists know. The basic approach is to enrich the input to a state-of-the-art Arabic morphological analyzer with word diacritics (full or partial) to enhance its performance. When applied to fully diacritized text, our approach produces annotations with an accuracy of over 97% on lemma, part-of-speech, and tokenization combined.

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A Proposal for a Part-of-Speech Tagset for the Albanian Language
Besim Kabashi | Thomas Proisl

Part-of-speech tagging is a basic step in Natural Language Processing that is often essential. Labeling the word forms of a text with fine-grained word-class information adds new value to it and can be a prerequisite for downstream processes like a dependency parser. Corpus linguists and lexicographers also benefit greatly from the improved search options that are available with tagged data. The Albanian language has some properties that pose difficulties for the creation of a part-of-speech tagset. In this paper, we discuss those difficulties and present a proposal for a part-of-speech tagset that can adequately represent the underlying linguistic phenomena.

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Using a Small Lexicon with CRFs Confidence Measure to Improve POS Tagging Accuracy
Mohamed Outahajala | Paolo Rosso

Like most of the languages which have only recently started being investigated for the Natural Language Processing (NLP) tasks, Amazigh lacks annotated corpora and tools and still suffers from the scarcity of linguistic tools and resources. The main aim of this paper is to present a new part-of-speech (POS) tagger based on a new Amazigh tag set (AMTS) composed of 28 tags. In line with our goal we have trained Conditional Random Fields (CRFs) to build a POS tagger for the Amazigh language. We have used the 10-fold technique to evaluate and validate our approach. The CRFs 10 folds average level is 87.95% and the best fold level result is 91.18%. In order to improve this result, we have gathered a set of about 8k words with their POS tags. The collected lexicon was used with CRFs confidence measure in order to have a more accurate POS-tagger. Hence, we have obtained a better performance of 93.82%.

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Learning from Within? Comparing PoS Tagging Approaches for Historical Text
Sarah Schulz | Jonas Kuhn

In this paper, we investigate unsupervised and semi-supervised methods for part-of-speech (PoS) tagging in the context of historical German text. We locate our research in the context of Digital Humanities where the non-canonical nature of text causes issues facing an Natural Language Processing world in which tools are mainly trained on standard data. Data deviating from the norm requires tools adjusted to this data. We explore to which extend the availability of such training material and resources related to it influences the accuracy of PoS tagging. We investigate a variety of algorithms including neural nets, conditional random fields and self-learning techniques in order to find the best-fitted approach to tackle data sparsity. Although methods using resources from related languages outperform weakly supervised methods using just a few training examples, we can still reach a promising accuracy with methods abstaining additional resources.

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Wow! What a Useful Extension! Introducing Non-Referential Concepts to Wordnet
Luis Morgado Da Costa | Francis Bond

In this paper we present the ongoing efforts to expand the depth and breath of the Open Multilingual Wordnet coverage by introducing two new classes of non-referential concepts to wordnet hierarchies: interjections and numeral classifiers. The lexical semantic hierarchy pioneered by Princeton Wordnet has traditionally restricted its coverage to referential and contentful classes of words: such as nouns, verbs, adjectives and adverbs. Previous efforts have been employed to enrich wordnet resources including, for example, the inclusion of pronouns, determiners and quantifiers within their hierarchies. Following similar efforts, and motivated by the ongoing semantic annotation of the NTU-Multilingual Corpus, we decided that the four traditional classes of words present in wordnets were too restrictive. Though non-referential, interjections and classifiers possess interesting semantics features that can be well captured by lexical resources like wordnets. In this paper, we will further motivate our decision to include non-referential concepts in wordnets and give an account of the current state of this expansion.

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SlangNet: A WordNet like resource for English Slang
Shehzaad Dhuliawala | Diptesh Kanojia | Pushpak Bhattacharyya

We present a WordNet like structured resource for slang words and neologisms on the internet. The dynamism of language is often an indication that current language technology tools trained on today’s data, may not be able to process the language in the future. Our resource could be (1) used to augment the WordNet, (2) used in several Natural Language Processing (NLP) applications which make use of noisy data on the internet like Information Retrieval and Web Mining. Such a resource can also be used to distinguish slang word senses from conventional word senses. To stimulate similar innovations widely in the NLP community, we test the efficacy of our resource for detecting slang using standard bag of words Word Sense Disambiguation (WSD) algorithms (Lesk and Extended Lesk) for English data on the internet.

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Discovering Fuzzy Synsets from the Redundancy in Different Lexical-Semantic Resources
Hugo Gonçalo Oliveira | Fábio Santos

Although represented as such in wordnets, word senses are not discrete. To handle word senses as fuzzy objects, we exploit the graph structure of synonymy pairs acquired from different sources to discover synsets where words have different membership degrees that reflect confidence. Following this approach, a wide-coverage fuzzy thesaurus was discovered from a synonymy network compiled from seven Portuguese lexical-semantic resources. Based on a crowdsourcing evaluation, we can say that the quality of the obtained synsets is far from perfect but, as expected in a confidence measure, it increases significantly for higher cut-points on the membership and, at a certain point, reaches 100% correction rate.

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The Hebrew FrameNet Project
Avi Hayoun | Michael Elhadad

We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data.

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An Open Corpus for Named Entity Recognition in Historic Newspapers
Clemens Neudecker

The availability of openly available textual datasets (“corpora”) with highly accurate manual annotations (“gold standard”) of named entities (e.g. persons, locations, organizations, etc.) is crucial in the training and evaluation of named entity recognition systems. Currently there are only few such datasets available on the web, and even less for texts containing historical spelling variation. The production and subsequent release into the public domain of four such datasets with 100 pages each for the languages Dutch, French, German (including Austrian) as part of the Europeana Newspapers project is expected to contribute to the further development and improvement of named entity recognition systems with a focus on historical content. This paper describes how these datasets were produced, what challenges were encountered in their creation and informs about their final quality and availability.

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Ambiguity Diagnosis for Terms in Digital Humanities
Béatrice Daille | Evelyne Jacquey | Gaël Lejeune | Luis Felipe Melo | Yannick Toussaint

Among all researches dedicating to terminology and word sense disambiguation, little attention has been devoted to the ambiguity of term occurrences. If a lexical unit is indeed a term of the domain, it is not true, even in a specialised corpus, that all its occurrences are terminological. Some occurrences are terminological and other are not. Thus, a global decision at the corpus level about the terminological status of all occurrences of a lexical unit would then be erroneous. In this paper, we propose three original methods to characterise the ambiguity of term occurrences in the domain of social sciences for French. These methods differently model the context of the term occurrences: one is relying on text mining, the second is based on textometry, and the last one focuses on text genre properties. The experimental results show the potential of the proposed approaches and give an opportunity to discuss about their hybridisation.

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Metrical Annotation of a Large Corpus of Spanish Sonnets: Representation, Scansion and Evaluation
Borja Navarro | María Ribes Lafoz | Noelia Sánchez

In order to analyze metrical and semantics aspects of poetry in Spanish with computational techniques, we have developed a large corpus annotated with metrical information. In this paper we will present and discuss the development of this corpus: the formal representation of metrical patterns, the semi-automatic annotation process based on a new automatic scansion system, the main annotation problems, and the evaluation, in which an inter-annotator agreement of 96% has been obtained. The corpus is open and available.

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Corpus Analysis based on Structural Phenomena in Texts: Exploiting TEI Encoding for Linguistic Research
Susanne Haaf

This paper poses the question, how linguistic corpus-based research may be enriched by the exploitation of conceptual text structures and layout as provided via TEI annotation. Examples for possible areas of research and usage scenarios are provided based on the German historical corpus of the Deutsches Textarchiv (DTA) project, which has been consistently tagged accordant to the TEI Guidelines, more specifically to the DTA ›Base Format‹ (DTABf). The paper shows that by including TEI-XML structuring in corpus-based analyses significances can be observed for different linguistic phenomena, as e.g. the development of conceptual text structures themselves, the syntactic embedding of terms in certain conceptual text structures, and phenomena of language change which become obvious via the layout of a text. The exemplary study carried out here shows some of the potential for the exploitation of TEI annotation for linguistic research, which might be kept in mind when making design decisions for new corpora as well when working with existing TEI corpora.

Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart the strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets.

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Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection
Daniel Preoţiuc-Pietro | P. K. Srijith | Mark Hepple | Trevor Cohn

Streaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral clustering approach to find groups of mutually informative terms occurring in discrete time frames. Experiments on large datasets of tweets show that these groups identify key real world events as they occur in time, despite no explicit supervision. The performance of our method rivals state-of-the-art methods for event detection on F-score, obtaining higher recall at the expense of precision.

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Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming
Luis Gerardo Mojica de la Vega | Vincent Ng

Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers who desire to employ these joint inference frameworks do not necessarily have to understand their theoretical underpinnings, it is imperative that they understand which of them should be applied under what circumstances. With the goal of helping NLP researchers better understand the relative strengths and weaknesses of MLNs and ILP; we will compare them along different dimensions of interest, such as expressiveness, ease of use, scalability, and performance. To our knowledge, this is the first systematic comparison of ILP and MLNs on an NLP task.

A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.

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Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction
Dain Kaplan | Neil Rubens | Simone Teufel | Takenobu Tokunaga

Active learning (AL) is often used in corpus construction (CC) for selecting “informative” documents for annotation. This is ideal for focusing annotation efforts when all documents cannot be annotated, but has the limitation that it is carried out in a closed-loop, selecting points that will improve an existing model. For phenomena-driven and exploratory CC, the lack of existing-models and specific task(s) for using it make traditional AL inapplicable. In this paper we propose a novel method for model-free AL utilising characteristics of phenomena for applying AL to select documents for annotation. The method can also supplement traditional closed-loop AL-based CC to extend the utility of the corpus created beyond a single task. We introduce our tool, MOVE, and show its potential with a real world case-study.

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QUEMDISSE? Reported speech in Portuguese
Cláudia Freitas | Bianca Freitas | Diana Santos

This paper presents some work on direct and indirect speech in Portuguese using corpus-based methods: we report on a study whose aim was to identify (i) Portuguese verbs used to introduce reported speech and (ii) syntactic patterns used to convey reported speech, in order to enhance the performance of a quotation extraction system, dubbed QUEMDISSE?. In addition, (iii) we present a Portuguese corpus annotated with reported speech, using the lexicon and rules provided by (i) and (ii), and discuss the process of their annotation and what was learned.

In this paper, we present the NewsReader MEANTIME corpus, a semantically annotated corpus of Wikinews articles. The corpus consists of 480 news articles, i.e. 120 English news articles and their translations in Spanish, Italian, and Dutch. MEANTIME contains annotations at different levels. The document-level annotation includes markables (e.g. entity mentions, event mentions, time expressions, and numerical expressions), relations between markables (modeling, for example, temporal information and semantic role labeling), and entity and event intra-document coreference. The corpus-level annotation includes entity and event cross-document coreference. Semantic annotation on the English section was performed manually; for the annotation in Italian, Spanish, and (partially) Dutch, a procedure was devised to automatically project the annotations on the English texts onto the translated texts, based on the manual alignment of the annotated elements; this enabled us not only to speed up the annotation process but also provided cross-lingual coreference. The English section of the corpus was extended with timeline annotations for the SemEval 2015 TimeLine shared task. The “First CLIN Dutch Shared Task” at CLIN26 was based on the Dutch section, while the EVALITA 2016 FactA (Event Factuality Annotation) shared task, based on the Italian section, is currently being organized.

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The ACQDIV Database: Min(d)ing the Ambient Language
Steven Moran

One of the most pressing questions in cognitive science remains unanswered: what cognitive mechanisms enable children to learn any of the world’s 7000 or so languages? Much discovery has been made with regard to specific learning mechanisms in specific languages, however, given the remarkable diversity of language structures (Evans and Levinson 2009, Bickel 2014) the burning question remains: what are the underlying processes that make language acquisition possible, despite substantial cross-linguistic variation in phonology, morphology, syntax, etc.? To investigate these questions, a comprehensive cross-linguistic database of longitudinal child language acquisition corpora from maximally diverse languages has been built.

Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry. In this paper we look into inter-annotator agreement of agent behaviour dimensions on two call center corpora. We find that the task can be annotated consistently over time, but that subjectivity issues impacts the quality of the annotation. The reformulation of some of the annotated dimensions is suggested in order to improve agreement.

In 2016, we set about building a large-scale corpus of everyday Japanese conversation―a collection of conversations embedded in naturally occurring activities in daily life. We will collect more than 200 hours of recordings over six years,publishing the corpus in 2022. To construct such a huge corpus, we have conducted a pilot project, one of whose purposes is to establish a corpus design for collecting various kinds of everyday conversations in a balanced manner. For this purpose, we conducted a survey of everyday conversational behavior, with about 250 adults, in order to reveal how diverse our everyday conversational behavior is and to build an empirical foundation for corpus design. The questionnaire included when, where, how long,with whom, and in what kind of activity informants were engaged in conversations. We found that ordinary conversations show the following tendencies: i) they mainly consist of chats, business talks, and consultations; ii) in general, the number of participants is small and the duration of the conversation is short; iii) many conversations are conducted in private places such as homes, as well as in public places such as offices and schools; and iv) some questionnaire items are related to each other. This paper describes an overview of this survey study, and then discusses how to design a large-scale corpus of everyday Japanese conversation on this basis.

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A Multi-party Multi-modal Dataset for Focus of Visual Attention in Human-human and Human-robot Interaction
Kalin Stefanov | Jonas Beskow

This papers describes a data collection setup and a newly recorded dataset. The main purpose of this dataset is to explore patterns in the focus of visual attention of humans under three different conditions - two humans involved in task-based interaction with a robot; same two humans involved in task-based interaction where the robot is replaced by a third human, and a free three-party human interaction. The dataset contains two parts - 6 sessions with duration of approximately 3 hours and 9 sessions with duration of approximately 4.5 hours. Both parts of the dataset are rich in modalities and recorded data streams - they include the streams of three Kinect v2 devices (color, depth, infrared, body and face data), three high quality audio streams, three high resolution GoPro video streams, touch data for the task-based interactions and the system state of the robot. In addition, the second part of the dataset introduces the data streams from three Tobii Pro Glasses 2 eye trackers. The language of all interactions is English and all data streams are spatially and temporally aligned.

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Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it
Rob Abbott | Brian Ecker | Pranav Anand | Marilyn Walker

Large scale corpora have benefited many areas of research in natural language processing, but until recently, resources for dialogue have lagged behind. Now, with the emergence of large scale social media websites incorporating a threaded dialogue structure, content feedback, and self-annotation (such as stance labeling), there are valuable new corpora available to researchers. In previous work, we released the INTERNET ARGUMENT CORPUS, one of the first larger scale resources available for opinion sharing dialogue. We now release the INTERNET ARGUMENT CORPUS 2.0 (IAC 2.0) in the hope that others will find it as useful as we have. The IAC 2.0 provides more data than IAC 1.0 and organizes it using an extensible, repurposable SQL schema. The database structure in conjunction with the associated code facilitates querying from and combining multiple dialogically structured data sources. The IAC 2.0 schema provides support for forum posts, quotations, markup (bold, italic, etc), and various annotations, including Stanford CoreNLP annotations. We demonstrate the generalizablity of the schema by providing code to import the ConVote corpus.

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Capturing Chat: Annotation and Tools for Multiparty Casual Conversation.
Emer Gilmartin | Nick Campbell

Casual multiparty conversation is an understudied but very common genre of spoken interaction, whose analysis presents a number of challenges in terms of data scarcity and annotation. We describe the annotation process used on the d64 and DANS multimodal corpora of multiparty casual talk, which have been manually segmented, transcribed, annotated for laughter and disfluencies, and aligned using the Penn Aligner. We also describe a visualization tool, STAVE, developed during the annotation process, which allows long stretches of talk or indeed entire conversations to be viewed, aiding preliminary identification of features and patterns worthy of analysis. It is hoped that this tool will be of use to other researchers working in this field.

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Privacy Issues in Online Machine Translation Services - European Perspective
Pawel Kamocki | Jim O’Regan

In order to develop its full potential, global communication needs linguistic support systems such as Machine Translation (MT). In the past decade, free online MT tools have become available to the general public, and the quality of their output is increasing. However, the use of such tools may entail various legal implications, especially as far as processing of personal data is concerned. This is even more evident if we take into account that their business model is largely based on providing translation in exchange for data, which can subsequently be used to improve the translation model, but also for commercial purposes. The purpose of this paper is to examine how free online MT tools fit in the European data protection framework, harmonised by the EU Data Protection Directive. The perspectives of both the user and the MT service provider are taken into account.

This paper introduces a novel research tool for the field of linguistics: The Lin|gu|is|tik web portal provides a virtual library which offers scientific information on every linguistic subject. It comprises selected internet sources and databases as well as catalogues for linguistic literature, and addresses an interdisciplinary audience. The virtual library is the most recent outcome of the Special Subject Collection Linguistics of the German Research Foundation (DFG), and also integrates the knowledge accumulated in the Bibliography of Linguistic Literature. In addition to the portal, we describe long-term goals and prospects with a special focus on ongoing efforts regarding an extension towards integrating language resources and Linguistic Linked Open Data.

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Towards a Language Service Infrastructure for Mobile Environments
Ngoc Nguyen | Donghui Lin | Takao Nakaguchi | Toru Ishida

Since mobile devices have feature-rich configurations and provide diverse functions, the use of mobile devices combined with the language resources of cloud environments is high promising for achieving a wide range communication that goes beyond the current language barrier. However, there are mismatches between using resources of mobile devices and services in the cloud such as the different communication protocol and different input and output methods. In this paper, we propose a language service infrastructure for mobile environments to combine these services. The proposed language service infrastructure allows users to use and mashup existing language resources on both cloud environments and their mobile devices. Furthermore, it allows users to flexibly use services in the cloud or services on mobile devices in their composite service without implementing several different composite services that have the same functionality. A case study of Mobile Shopping Translation System using both a service in the cloud (translation service) and services on mobile devices (Bluetooth low energy (BLE) service and text-to-speech service) is introduced.

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Designing A Long Lasting Linguistic Project: The Case Study of ASIt
Maristella Agosti | Emanuele Di Buccio | Giorgio Maria Di Nunzio | Cecilia Poletto | Esther Rinke

In this paper, we discuss the requirements that a long lasting linguistic database should have in order to meet the needs of the linguists together with the aim of durability and sharing of data. In particular, we discuss the generalizability of the Syntactic Atlas of Italy, a linguistic project that builds on a long standing tradition of collecting and analyzing linguistic corpora, on a more recent project that focuses on the synchronic and diachronic analysis of the syntax of Italian and Portuguese relative clauses. The results that are presented are in line with the FLaReNet Strategic Agenda that highlighted the most pressing needs for research areas, such as Natural Language Processing, and presented a set of recommendations for the development and progress of Language resources in Europe.

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Global Open Resources and Information for Language and Linguistic Analysis (GORILLA)
Damir Cavar | Malgorzata Cavar | Lwin Moe

The infrastructure Global Open Resources and Information for Language and Linguistic Analysis (GORILLA) was created as a resource that provides a bridge between disciplines such as documentary, theoretical, and corpus linguistics, speech and language technologies, and digital language archiving services. GORILLA is designed as an interface between digital language archive services and language data producers. It addresses various problems of common digital language archive infrastructures. At the same time it serves the speech and language technology communities by providing a platform to create and share speech and language data from low-resourced and endangered languages. It hosts an initial collection of language models for speech and natural language processing (NLP), and technologies or software tools for corpus creation and annotation. GORILLA is designed to address the Transcription Bottleneck in language documentation, and, at the same time to provide solutions to the general Language Resource Bottleneck in speech and language technologies. It does so by facilitating the cooperation between documentary and theoretical linguistics, and speech and language technologies research and development, in particular for low-resourced and endangered languages.

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corpus-tools.org: An Interoperable Generic Software Tool Set for Multi-layer Linguistic Corpora
Stephan Druskat | Volker Gast | Thomas Krause | Florian Zipser

This paper introduces an open source, interoperable generic software tool set catering for the entire workflow of creation, migration, annotation, query and analysis of multi-layer linguistic corpora. It consists of four components: Salt, a graph-based meta model and API for linguistic data, the common data model for the rest of the tool set; Pepper, a conversion tool and platform for linguistic data that can be used to convert many different linguistic formats into each other; Atomic, an extensible, platform-independent multi-layer desktop annotation software for linguistic corpora; ANNIS, a search and visualization architecture for multi-layer linguistic corpora with many different visualizations and a powerful native query language. The set was designed to solve the following issues in a multi-layer corpus workflow: Lossless data transition between tools through a common data model generic enough to allow for a potentially unlimited number of different types of annotation, conversion capabilities for different linguistic formats to cater for the processing of data from different sources and/or with existing annotations, a high level of extensibility to enhance the sustainability of the whole tool set, analysis capabilities encompassing corpus and annotation query alongside multi-faceted visualizations of all annotation layers.

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CommonCOW: Massively Huge Web Corpora from CommonCrawl Data and a Method to Distribute them Freely under Restrictive EU Copyright Laws
Roland Schäfer

In this paper, I describe a method of creating massively huge web corpora from the CommonCrawl data sets and redistributing the resulting annotations in a stand-off format. Current EU (and especially German) copyright legislation categorically forbids the redistribution of downloaded material without express prior permission by the authors. Therefore, such stand-off annotations (or other derivates) are the only format in which European researchers (like myself) are allowed to re-distribute the respective corpora. In order to make the full corpora available to the public despite such restrictions, the stand-off format presented here allows anybody to locally reconstruct the full corpora with the least possible computational effort.

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CLARIN-EL Web-based Annotation Tool
Ioannis Manousos Katakis | Georgios Petasis | Vangelis Karkaletsis

This paper presents a new Web-based annotation tool, the “CLARIN-EL Web-based Annotation Tool”. Based on an existing annotation infrastructure offered by the “Ellogon” language enginneering platform, this new tool transfers a large part of Ellogon’s features and functionalities to a Web environment, by exploiting the capabilities of cloud computing. This new annotation tool is able to support a wide range of annotation tasks, through user provided annotation schemas in XML. The new annotation tool has already been employed in several annotation tasks, including the anotation of arguments, which is presented as a use case. The CLARIN-EL annotation tool is compared to existing solutions along several dimensions and features. Finally, future work includes the improvement of integration with the CLARIN-EL infrastructure, and the inclusion of features not currently supported, such as the annotation of aligned documents.

This paper presents two alternative NLP architectures to analyze massive amounts of documents, using parallel processing. The two architectures focus on different processing scenarios, namely batch-processing and streaming processing. The batch-processing scenario aims at optimizing the overall throughput of the system, i.e., minimizing the overall time spent on processing all documents. The streaming architecture aims to minimize the time to process real-time incoming documents and is therefore especially suitable for live feeds. The paper presents experiments with both architectures, and reports the overall gain when they are used for batch as well as for streaming processing. All the software described in the paper is publicly available under free licenses.

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Publishing the Trove Newspaper Corpus
Steve Cassidy

The Trove Newspaper Corpus is derived from the National Library of Australia’s digital archive of newspaper text. The corpus is a snapshot of the NLA collection taken in 2015 to be made available for language research as part of the Alveo Virtual Laboratory and contains 143 million articles dating from 1806 to 2007. This paper describes the work we have done to make this large corpus available as a research collection, facilitating access to individual documents and enabling large scale processing of the newspaper text in a cloud-based environment.

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New Developments in the LRE Map
Vladimir Popescu | Lin Liu | Riccardo Del Gratta | Khalid Choukri | Nicoletta Calzolari

In this paper we describe the new developments brought to LRE Map, especially in terms of the user interface of the Web application, of the searching of the information therein, and of the data model updates.

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Hidden Resources ― Strategies to Acquire and Exploit Potential Spoken Language Resources in National Archives
Jens Edlund | Joakim Gustafson

In 2014, the Swedish government tasked a Swedish agency, The Swedish Post and Telecom Authority (PTS), with investigating how to best create and populate an infrastructure for spoken language resources (Ref N2014/2840/ITP). As a part of this work, the department of Speech, Music and Hearing at KTH Royal Institute of Technology have taken inventory of existing potential spoken language resources, mainly in Swedish national archives and other governmental or public institutions. In this position paper, key priorities, perspectives, and strategies that may be of general, rather than Swedish, interest are presented. We discuss broad types of potential spoken language resources available; to what extent these resources are free to use; and thirdly the main contribution: strategies to ensure the continuous acquisition of spoken language resources in a manner that facilitates speech and speech technology research.

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The ELRA License Wizard
Valérie Mapelli | Vladimir Popescu | Lin Liu | Meritxell Fernández Barrera | Khalid Choukri

To allow an easy understanding of the various licenses that exist for the use of Language Resources (ELRA’s, META-SHARE’s, Creative Commons’, etc.), ELRA has developed a License Wizardto help the right-holders share/distribute their resources under the appropriate license. It also aims to be exploited by users to better understand the legal obligations that apply in various licensing situations. The present paper elaborates on the License Wizard functionalities of this web configurator, which enables to select a number of legal features and obtain the user license adapted to the users selection, to define which user licenses they would like to select in order to distribute their Language Resources, to integrate the user license terms into a Distribution Agreement that could be proposed to ELRA or META-SHARE for further distribution through the ELRA Catalogue of Language Resources. Thanks to a flexible back office, the structure of the legal feature selection can easily be reviewed to include other features that may be relevant for other licenses. Integrating contributions from other initiatives thus aim to be one of the obvious next steps, with a special focus on CLARIN and Linked Data experiences.

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Review on the Existing Language Resources for Languages of France
Thibault Grouas | Valérie Mapelli | Quentin Samier

With the support of the DGLFLF, ELDA conducted an inventory of existing language resources for the regional languages of France. The main aim of this inventory was to assess the exploitability of the identified resources within technologies. A total of 2,299 Language Resources were identified. As a second step, a deeper analysis of a set of three language groups (Breton, Occitan, overseas languages) was carried out along with a focus of their exploitability within three technologies: automatic translation, voice recognition/synthesis and spell checkers. The survey was followed by the organisation of the TLRF2015 Conference which aimed to present the state of the art in the field of the Technologies for Regional Languages of France. The next step will be to activate the network of specialists built up during the TLRF conference and to begin the organisation of a second TLRF conference. Meanwhile, the French Ministry of Culture continues its actions related to linguistic diversity and technology, in particular through a project with Wikimedia France related to contributions to Wikipedia in regional languages, the upcoming new version of the “Corpus de la Parole” and the reinforcement of the DGLFLF’s Observatory of Linguistic Practices.

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Selection Criteria for Low Resource Language Programs
Christopher Cieri | Mike Maxwell | Stephanie Strassel | Jennifer Tracey

This paper documents and describes the criteria used to select languages for study within programs that include low resource languages whether given that label or another similar one. It focuses on five US common task, Human Language Technology research and development programs in which the authors have provided information or consulting related to the choice of language. The paper does not describe the actual selection process which is the responsibility of program management and highly specific to a program’s individual goals and context. Instead it concentrates on the data and criteria that have been considered relevant previously with the thought that future program managers and their consultants may adapt these and apply them with different prioritization to future programs.

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Enhancing Cross-border EU E-commerce through Machine Translation: Needed Language Resources, Challenges and Opportunities
Meritxell Fernández Barrera | Vladimir Popescu | Antonio Toral | Federico Gaspari | Khalid Choukri

This paper discusses the role that statistical machine translation (SMT) can play in the development of cross-border EU e-commerce,by highlighting extant obstacles and identifying relevant technologies to overcome them. In this sense, it firstly proposes a typology of e-commerce static and dynamic textual genres and it identifies those that may be more successfully targeted by SMT. The specific challenges concerning the automatic translation of user-generated content are discussed in detail. Secondly, the paper highlights the risk of data sparsity inherent to e-commerce and it explores the state-of-the-art strategies to achieve domain adequacy via adaptation. Thirdly, it proposes a robust workflow for the development of SMT systems adapted to the e-commerce domain by relying on inexpensive methods. Given the scarcity of user-generated language corpora for most language pairs, the paper proposes to obtain monolingual target-language data to train language models and aligned parallel corpora to tune and evaluate MT systems by means of crowdsourcing.

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Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
Enrico Santus | Alessandro Lenci | Tin-Shing Chiu | Qin Lu | Chu-Ren Huang

ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a Random Forest algorithm and nine unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2% (vector cosine). When the classification is binary, ROOT9 achieves the following results against the baseline. hypernyms-co-hyponyms 95.7% vs. 69.8%, hypernyms-random 91.8% vs. 64.1% and co-hyponyms-random 97.8% vs. 79.4%. In order to compare the performance with the state-of-the-art, we have also evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it is in fact competitive. Finally, we investigated whether the system learns the semantic relation or it simply learns the prototypical hypernyms, as claimed by Levy et al. (2015). The second possibility seems to be the most likely, even though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to drastically reduce this bias.

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What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
Enrico Santus | Alessandro Lenci | Tin-Shing Chiu | Qin Lu | Chu-Ren Huang

In this paper, we claim that Vector Cosine ― which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models ― can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists. This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words. To prove it, we describe and evaluate APSyn, a variant of Average Precision that ― independently of the adopted parameters ― outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50%) and several state-of-the-art approaches.

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Assessing the Potential of Metaphoricity of verbs using corpus data
Marco Del Tredici | Núria Bel

The paper investigates the relation between metaphoricity and distributional characteristics of verbs, introducing POM, a corpus-derived index that can be used to define the upper bound of metaphoricity of any expression in which a given verb occurs. The work moves from the observation that while some verbs can be used to create highly metaphoric expressions, others can not. We conjecture that this fact is related to the number of contexts in which a verb occurs and to the frequency of each context. This intuition is modelled by introducing a method in which each context of a verb in a corpus is assigned a vector representation, and a clustering algorithm is employed to identify similar contexts. Eventually, the Standard Deviation of the relative frequency values of the clusters is computed and taken as the POM of the target verb. We tested POM in two experimental settings obtaining values of accuracy of 84% and 92%. Since we are convinced, along with (Shutoff, 2015), that metaphor detection systems should be concerned only with the identification of highly metaphoric expressions, we believe that POM could be profitably employed by these systems to a priori exclude expressions that, due to the verb they include, can only have low degrees of metaphoricity

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Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports
Mathieu Lafourcade | Lionel Ramadier

This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts...) between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of)) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence néoplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r-lieu breast. An evaluation of the effect of semantic constraints is proposed.

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EVALution-MAN: A Chinese Dataset for the Training and Evaluation of DSMs
Liu Hongchao | Karl Neergaard | Enrico Santus | Chu-Ren Huang

Distributional semantic models (DSMs) are currently being used in the measurement of word relatedness and word similarity. One shortcoming of DSMs is that they do not provide a principled way to discriminate different semantic relations. Several approaches have been adopted that rely on annotated data either in the training of the model or later in its evaluation. In this paper, we introduce a dataset for training and evaluating DSMs on semantic relations discrimination between words, in Mandarin, Chinese. The construction of the dataset followed EVALution 1.0, which is an English dataset for the training and evaluating of DSMs. The dataset contains 360 relation pairs, distributed in five different semantic relations, including antonymy, synonymy, hypernymy, meronymy and nearsynonymy. All relation pairs were checked manually to estimate their quality. In the 360 word relation pairs, there are 373 relata. They were all extracted and subsequently manually tagged according to their semantic type. The relatas’ frequency was calculated in a combined corpus of Sinica and Chinese Gigaword. To the best of our knowledge, EVALution-MAN is the first of its kind for Mandarin, Chinese.

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Towards Building Semantic Role Labeler for Indian Languages
Maaz Anwar | Dipti Sharma

We present a statistical system for identifying the semantic relationships or semantic roles for two major Indian Languages, Hindi and Urdu. Given an input sentence and a predicate/verb, the system first identifies the arguments pertaining to that verb and then classifies it into one of the semantic labels which can either be a DOER, THEME, LOCATIVE, CAUSE, PURPOSE etc. The system is based on 2 statistical classifiers trained on roughly 130,000 words for Urdu and 100,000 words for Hindi that were hand-annotated with semantic roles under the PropBank project for these two languages. Our system achieves an accuracy of 86% in identifying the arguments of a verb for Hindi and 75% for Urdu. At the subsequent task of classifying the constituents into their semantic roles, the Hindi system achieved 58% precision and 42% recall whereas Urdu system performed better and achieved 83% precision and 80% recall. Our study also allowed us to compare the usefulness of different linguistic features and feature combinations in the semantic role labeling task. We also examine the use of statistical syntactic parsing as feature in the role labeling task.

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A Framework for Automatic Acquisition of Croatian and Serbian Verb Aspect from Corpora
Tanja Samardžić | Maja Miličević

Verb aspect is a grammatical and lexical category that encodes temporal unfolding and duration of events described by verbs. It is a potentially interesting source of information for various computational tasks, but has so far not been studied in much depth from the perspective of automatic processing. Slavic languages are particularly interesting in this respect, as they encode aspect through complex and not entirely consistent lexical derivations involving prefixation and suffixation. Focusing on Croatian and Serbian, in this paper we propose a novel framework for automatic classification of their verb types into a number of fine-grained aspectual classes based on the observable morphology of verb forms. In addition, we provide a set of around 2000 verbs classified based on our framework. This set can be used for linguistic research as well as for testing automatic classification on a larger scale. With minor adjustments the approach is also applicable to other Slavic languages

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Monolingual Social Media Datasets for Detecting Contradiction and Entailment
Piroska Lendvai | Isabelle Augenstein | Kalina Bontcheva | Thierry Declerck

Entailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined. We report on a new 3-way judgement Recognizing Textual Entailment (RTE) resource that originates in the Social Media domain, and explain our semi-automatic creation method for the special purpose of information verification, which draws on manually established rumourous claims reported during crisis events. From about 500 English tweets related to 70 unique claims we compile and evaluate 5.4k RTE pairs, while continue automatizing the workflow to generate similar-sized datasets in other languages.

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VoxML: A Visualization Modeling Language
James Pustejovsky | Nikhil Krishnaswamy

We present the specification for a modeling language, VoxML, which encodes semantic knowledge of real-world objects represented as three-dimensional models, and of events and attributes related to and enacted over these objects. VoxML is intended to overcome the limitations of existing 3D visual markup languages by allowing for the encoding of a broad range of semantic knowledge that can be exploited by a variety of systems and platforms, leading to multimodal simulations of real-world scenarios using conceptual objects that represent their semantic values

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Metonymy Analysis Using Associative Relations between Words
Takehiro Teraoka

Metonymy is a figure of speech in which one item’s name represents another item that usually has a close relation with the first one. Metonymic expressions need to be correctly detected and interpreted because sentences including such expressions have different mean- ings from literal ones; computer systems may output inappropriate results in natural language processing. In this paper, an associative approach for analyzing metonymic expressions is proposed. By using associative information and two conceptual distances between words in a sentence, a previous method is enhanced and a decision tree is trained to detect metonymic expressions. After detecting these expressions, they are interpreted as metonymic understanding words by using associative information. This method was evaluated by comparing it with two baseline methods based on previous studies on the Japanese language that used case frames and co-occurrence information. As a result, the proposed method exhibited significantly better accuracy (0.85) of determining words as metonymic or literal expressions than the baselines. It also exhibited better accuracy (0.74) of interpreting the detected metonymic expressions than the baselines.

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Embedding Open-domain Common-sense Knowledge from Text
Travis Goodwin | Sanda Harabagiu

Our ability to understand language often relies on common-sense knowledge ― background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language.

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Medical Concept Embeddings via Labeled Background Corpora
Eneldo Loza Mencía | Gerard de Melo | Jinseok Nam

In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words. Among other things, these facilitate computing word similarity and relatedness scores. The most well-known example of algorithms to produce representations of this sort are the word2vec approaches. In this paper, we investigate a new model to induce such vector spaces for medical concepts, based on a joint objective that exploits not only word co-occurrences but also manually labeled documents, as available from sources such as PubMed. Our extensive experimental analysis shows that our embeddings lead to significantly higher correlations with human similarity and relatedness assessments than previous work. Due to the simplicity and versatility of vector representations, these findings suggest that our resource can easily be used as a drop-in replacement to improve any systems relying on medical concept similarity measures.

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Question-Answering with Logic Specific to Video Games
Corentin Dumont | Ran Tian | Kentaro Inui

We present a corpus and a knowledge database aiming at developing Question-Answering in a new context, the open world of a video game. We chose a popular game called ‘Minecraft’, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database. We are interested in the logic rules specific to the game, which may not exist in the real world. The ultimate goal of this research is to build a QA system that can answer natural language questions from players by using inference on these game-specific logic rules. The QA corpus is partially composed of online quiz questions and partially composed of manually written variations of the most relevant ones. The knowledge database is extracted from several wiki-like websites about Minecraft. It is composed of unstructured data, such as text, that will be structured using the meaning representation we defined, and already structured data such as infoboxes. A preliminary examination of the data shows that players are asking creative questions about the game, and that the QA corpus can be used for clustering verbs and linking them to predefined actions in the game.

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Mining the Spoken Wikipedia for Speech Data and Beyond
Arne Köhn | Florian Stegen | Timo Baumann

We present a corpus of time-aligned spoken data of Wikipedia articles as well as the pipeline that allows to generate such corpora for many languages. There are initiatives to create and sustain spoken Wikipedia versions in many languages and hence the data is freely available, grows over time, and can be used for automatic corpus creation. Our pipeline automatically downloads and aligns this data. The resulting German corpus currently totals 293h of audio, of which we align 71h in full sentences and another 86h of sentences with some missing words. The English corpus consists of 287h, for which we align 27h in full sentence and 157h with some missing words. Results are publically available.

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A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation
Robert Herms | Laura Seelig | Stefanie Münch | Maximilian Eibl

In this Paper we present a corpus named SXUCorpus which contains read and spontaneous speech of the Upper Saxon German dialect. The data has been collected from eight archives of local television stations located in the Free State of Saxony. The recordings include broadcasted topics of news, economy, weather, sport, and documentation from the years 1992 to 1996 and have been manually transcribed and labeled. In the paper, we report the methodology of collecting and processing analog audiovisual material, constructing the corpus and describe the properties of the data. In its current version, the corpus is available to the scientific community and is designed for automatic speech recognition (ASR) evaluation with a development set and a test set. We performed ASR experiments with the open-source framework sphinx-4 including a configuration for Standard German on the dataset. Additionally, we show the influence of acoustic model and language model adaptation by the utilization of the development set.

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This paper presents the TYPALOC corpus of French Dysarthric and Healthy speech and the rationale underlying its constitution. The objective is to compare phonetic variation in the speech of dysarthric vs. healthy speakers in different speech conditions (read and unprepared speech). More precisely, we aim to compare the extent, types and location of phonetic variation within these different populations and speech conditions. The TYPALOC corpus is constituted of a selection of 28 dysarthric patients (three different pathologies) and of 12 healthy control speakers recorded while reading the same text and in a more natural continuous speech condition. Each audio signal has been segmented into Inter-Pausal Units. Then, the corpus has been manually transcribed and automatically aligned. The alignment has been corrected by an expert phonetician. Moreover, the corpus benefits from an automatic syllabification and an Automatic Detection of Acoustic Phone-Based Anomalies. Finally, in order to interpret phonetic variations due to pathologies, a perceptual evaluation of each patient has been conducted. Quantitative data are provided at the end of the paper.

We present a new speech database containing 18.5 hours of annotated radio broadcasts in the Frisian language. Frisian is mostly spoken in the province Fryslan and it is the second official language of the Netherlands. The recordings are collected from the archives of Omrop Fryslan, the regional public broadcaster of the province Fryslan. The database covers almost a 50-year time span. The native speakers of Frisian are mostly bilingual and often code-switch in daily conversations due to the extensive influence of the Dutch language. Considering the longitudinal and code-switching nature of the data, an appropriate annotation protocol has been designed and the data is manually annotated with the orthographic transcription, speaker identities, dialect information, code-switching details and background noise/music information.

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The SI TEDx-UM speech database: a new Slovenian Spoken Language Resource
Andrej Žgank | Mirjam Sepesy Maučec | Darinka Verdonik

This paper presents a new Slovenian spoken language resource built from TEDx Talks. The speech database contains 242 talks in total duration of 54 hours. The annotation and transcription of acquired spoken material was generated automatically, applying acoustic segmentation and automatic speech recognition. The development and evaluation subset was also manually transcribed using the guidelines specified for the Slovenian GOS corpus. The manual transcriptions were used to evaluate the quality of unsupervised transcriptions. The average word error rate for the SI TEDx-UM evaluation subset was 50.7%, with out of vocabulary rate of 24% and language model perplexity of 390. The unsupervised transcriptions contain 372k tokens, where 32k of them were different.

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Speech Corpus Spoken by Young-old, Old-old and Oldest-old Japanese
Yurie Iribe | Norihide Kitaoka | Shuhei Segawa

We have constructed a new speech data corpus, using the utterances of 100 elderly Japanese people, to improve speech recognition accuracy of the speech of older people. Humanoid robots are being developed for use in elder care nursing homes. Interaction with such robots is expected to help maintain the cognitive abilities of nursing home residents, as well as providing them with companionship. In order for these robots to interact with elderly people through spoken dialogue, a high performance speech recognition system for speech of elderly people is needed. To develop such a system, we recorded speech uttered by 100 elderly Japanese, most of them are living in nursing homes, with an average age of 77.2. Previously, a seniors’ speech corpus named S-JNAS was developed, but the average age of the participants was 67.6 years, but the target age for nursing home care is around 75 years old, much higher than that of the S-JNAS samples. In this paper we compare our new corpus with an existing Japanese read speech corpus, JNAS, which consists of adult speech, and with the above mentioned S-JNAS, the senior version of JNAS.

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Polish Rhythmic Database ― New Resources for Speech Timing and Rhythm Analysis
Agnieszka Wagner | Katarzyna Klessa | Jolanta Bachan

This paper reports on a new database ― Polish rhythmic database and tools developed with the aim of investigating timing phenomena and rhythmic structure in Polish including topics such as, inter alia, the effect of speaking style and tempo on timing patterns, phonotactic and phrasal properties of speech rhythm and stability of rhythm metrics. So far, 19 native and 12 non-native speakers with different first languages have been recorded. The collected speech data (5 h 14 min.) represents five different speaking styles and five different tempi. For the needs of speech corpus management, annotation and analysis, a database was developed and integrated with Annotation Pro (Klessa et al., 2013, Klessa, 2016). Currently, the database is the only resource for Polish which allows for a systematic study of a broad range of phenomena related to speech timing and rhythm. The paper also introduces new tools and methods developed to facilitate the database annotation and analysis with respect to various timing and rhythm measures. In the end, the results of an ongoing research and first experimental results using the new resources are reported and future work is sketched.

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An Extension of the Slovak Broadcast News Corpus based on Semi-Automatic Annotation
Peter Viszlay | Ján Staš | Tomáš Koctúr | Martin Lojka | Jozef Juhár

In this paper, we introduce an extension of our previously released TUKE-BNews-SK corpus based on a semi-automatic annotation scheme. It firstly relies on the automatic transcription of the BN data performed by our Slovak large vocabulary continuous speech recognition system. The generated hypotheses are then manually corrected and completed by trained human annotators. The corpus is composed of 25 hours of fully-annotated spontaneous and prepared speech. In addition, we have acquired 900 hours of another BN data, part of which we plan to annotate semi-automatically. We present a preliminary corpus evaluation that gives very promising results.

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Generating a Yiddish Speech Corpus, Forced Aligner and Basic ASR System for the AHEYM Project
Malgorzata Ćavar | Damir Ćavar | Dov-Ber Kerler | Anya Quilitzsch

To create automatic transcription and annotation tools for the AHEYM corpus of recorded interviews with Yiddish speakers in Eastern Europe we develop initial Yiddish language resources that are used for adaptations of speech and language technologies. Our project aims at the development of resources and technologies that can make the entire AHEYM corpus and other Yiddish resources more accessible to not only the community of Yiddish speakers or linguists with language expertise, but also historians and experts from other disciplines or the general public. In this paper we describe the rationale behind our approach, the procedures and methods, and challenges that are not specific to the AHEYM corpus, but apply to all documentary language data that is collected in the field. To the best of our knowledge, this is the first attempt to create a speech corpus and speech technologies for Yiddish. This is also the first attempt to work out speech and language technologies to transcribe and translate a large collection of Yiddish spoken language resources.

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Coh-Metrix-Esp: A Complexity Analysis Tool for Documents Written in Spanish
Andre Quispesaravia | Walter Perez | Marco Sobrevilla Cabezudo | Fernando Alva-Manchego

Text Complexity Analysis is an useful task in Education. For example, it can help teachers select appropriate texts for their students according to their educational level. This task requires the analysis of several text features that people do mostly manually (e.g. syntactic complexity, words variety, etc.). In this paper, we present a tool useful for Complexity Analysis, called Coh-Metrix-Esp. This is the Spanish version of Coh-Metrix and is able to calculate 45 readability indices. We analyse how these indices behave in a corpus of “simple” and “complex” documents, and also use them as features in a complexity binary classifier for texts in Spanish. After some experiments with machine learning algorithms, we got 0.9 F-measure for a corpus that contains tales for kids and adults and 0.82 F-measure for a corpus with texts written for students of Spanish as a foreign language.