2021
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An Experiment on Implicitly Crowdsourcing Expert Knowledge about Romanian Synonyms from Language Learners
Lionel Nicolas
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Lavinia Nicoleta Aparaschivei
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Verena Lyding
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Christos Rodosthenous
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Federico Sangati
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Alexander König
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Corina Forascu
Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning
2020
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The Challenge of the TV game La Ghigliottina to NLP
Federico Sangati
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Antonio Pascucci
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Johanna Monti
Workshop on Games and Natural Language Processing
In this paper, we describe a Telegram bot, Mago della Ghigliottina (Ghigliottina Wizard), able to solve La Ghigliottina game (The Guillotine), the final game of the Italian TV quiz show L’Eredità. Our system relies on linguistic resources and artificial intelligence and achieves better results than human players (and competitors of L’Eredità too). In addition to solving a game, Mago della Ghigliottina can also generate new game instances and challenge the users to match the solution.
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Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning
Lionel Nicolas
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Verena Lyding
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Claudia Borg
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Corina Forascu
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Karën Fort
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Katerina Zdravkova
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Iztok Kosem
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Jaka Čibej
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Špela Arhar Holdt
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Alice Millour
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Alexander König
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Christos Rodosthenous
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Federico Sangati
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Umair ul Hassan
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Anisia Katinskaia
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Anabela Barreiro
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Lavinia Aparaschivei
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Yaakov HaCohen-Kerner
Proceedings of the Twelfth Language Resources and Evaluation Conference
We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of this generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs.
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Using Crowdsourced Exercises for Vocabulary Training to Expand ConceptNet
Christos Rodosthenous
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Verena Lyding
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Federico Sangati
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Alexander König
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Umair ul Hassan
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Lionel Nicolas
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Jolita Horbacauskiene
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Anisia Katinskaia
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Lavinia Aparaschivei
Proceedings of the Twelfth Language Resources and Evaluation Conference
In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and – in the background – to collect and evaluate the learners’ answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills.
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From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap
Giulia Speranza
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Maria Pia di Buono
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Johanna Monti
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Federico Sangati
Proceedings of the Twelfth Language Resources and Evaluation Conference
Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems. Nonetheless, with the exception of a few felicitous examples, such as the IATE (Interactive Terminology for Europe) Termbank, many terminological resources are not available in standard formats, such as Term Base eXchange (TBX), thus preventing their sharing and reuse. Yet, these terminologies could be improved associating the correspondent ontology-based information. The research described in the present contribution demonstrates the process and the methodologies adopted in the automatic conversion into TBX of such type of resources, together with their semantic enrichment based on the formalization of ontological information into terminologies. We present a proof-of-concept using the Italian Linguistic Resource for the Archaeological domain (developed according to Thesauri and Guidelines of the Italian Central Institute for the Catalogue and Documentation). Further, we introduce the conversion tool developed to support the process of creating ontology-aware terminologies for improving interoperability and sharing of existing language technologies and data sets.
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Substituto – A Synchronous Educational Language Game for Simultaneous Teaching and Crowdsourcing
Marianne Grace Araneta
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Gülşen Eryiğit
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Alexander König
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Ji-Ung Lee
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Ana Luís
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Verena Lyding
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Lionel Nicolas
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Christos Rodosthenous
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Federico Sangati
Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning
2019
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v-trel: Vocabulary Trainer for Tracing Word Relations - An Implicit Crowdsourcing Approach
Verena Lyding
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Christos Rodosthenous
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Federico Sangati
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Umair ul Hassan
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Lionel Nicolas
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Alexander König
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Jolita Horbacauskiene
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Anisia Katinskaia
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource. We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations. We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective. The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.
2017
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The PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions
Agata Savary
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Carlos Ramisch
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Silvio Cordeiro
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Federico Sangati
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Veronika Vincze
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Behrang QasemiZadeh
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Marie Candito
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Fabienne Cap
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Voula Giouli
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Ivelina Stoyanova
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Antoine Doucet
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
Multiword expressions (MWEs) are known as a “pain in the neck” for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one’s heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as “words with spaces”. We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.
2016
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PARSEME Survey on MWE Resources
Gyri Smørdal Losnegaard
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Federico Sangati
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Carla Parra Escartín
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Agata Savary
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Sascha Bargmann
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Johanna Monti
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
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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D(H)ante: A New Set of Tools for XIII Century Italian
Angelo Basile
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Federico Sangati
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
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.
2015
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Multiword Expression Identification with Recurring Tree Fragments and Association Measures
Federico Sangati
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Andreas van Cranenburgh
Proceedings of the 11th Workshop on Multiword Expressions
2013
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Incremental Tree Substitution Grammar for Parsing and Sentence Prediction
Federico Sangati
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Frank Keller
Transactions of the Association for Computational Linguistics, Volume 1
In this paper, we present the first incremental parser for Tree Substitution Grammar (TSG). A TSG allows arbitrarily large syntactic fragments to be combined into complete trees; we show how constraints (including lexicalization) can be imposed on the shape of the TSG fragments to enable incremental processing. We propose an efficient Earley-based algorithm for incremental TSG parsing and report an F-score competitive with other incremental parsers. In addition to whole-sentence F-score, we also evaluate the partial trees that the parser constructs for sentence prefixes; partial trees play an important role in incremental interpretation, language modeling, and psycholinguistics. Unlike existing parsers, our incremental TSG parser can generate partial trees that include predictions about the upcoming words in a sentence. We show that it outperforms an n-gram model in predicting more than one upcoming word.
2011
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Accurate Parsing with Compact Tree-Substitution Grammars: Double-DOP
Federico Sangati
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Willem Zuidema
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
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Discontinuous Data-Oriented Parsing: A mildly context-sensitive all-fragments grammar
Andreas van Cranenburgh
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Remko Scha
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Federico Sangati
Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages
2010
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A Probabilistic Generative Model for an Intermediate Constituency-Dependency Representation
Federico Sangati
Proceedings of the ACL 2010 Student Research Workshop
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How Spoken Language Corpora Can Refine Current Speech Motor Training Methodologies
Daniil Umanski
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Federico Sangati
Proceedings of the ACL 2010 Student Research Workshop
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Efficiently Extract Rrecurring Tree Fragments from Large Treebanks
Federico Sangati
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Willem Zuidema
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Rens Bod
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
In this paper we describe FragmentSeeker, a tool which is capable to identify all those tree constructions which are recurring multiple times in a large Phrase Structure treebank. The tool is based on an efficient kernel-based dynamic algorithm, which compares every pair of trees of a given treebank and computes the list of fragments which they both share. We describe two different notions of fragments we will use, i.e. standard and partial fragments, and provide the implementation details on how to extract them from a syntactically annotated corpus. We have tested our system on the Penn Wall Street Journal treebank for which we present quantitative and qualitative analysis on the obtained recurring structures, as well as provide empirical time performance. Finally we propose possible ways our tool could contribute to different research fields related to corpus analysis and processing, such as parsing, corpus statistics, annotation guidance, and automatic detection of argument structure.
2009
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Unsupervised Methods for Head Assignments
Federico Sangati
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Willem Zuidema
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)
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A generative re-ranking model for dependency parsing
Federico Sangati
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Willem Zuidema
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Rens Bod
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)