Massimo Poesio

Also published as: M. Poesio


2021

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We Need to Consider Disagreement in Evaluation
Valerio Basile | Michael Fell | Tommaso Fornaciari | Dirk Hovy | Silviu Paun | Barbara Plank | Massimo Poesio | Alexandra Uma
Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future

Evaluation is of paramount importance in data-driven research fields such as Natural Language Processing (NLP) and Computer Vision (CV). Current evaluation practice largely hinges on the existence of a single “ground truth” against which we can meaningfully compare the prediction of a model. However, this comparison is flawed for two reasons. 1) In many cases, more than one answer is correct. 2) Even where there is a single answer, disagreement among annotators is ubiquitous, making it difficult to decide on a gold standard. We argue that the current methods of adjudication, agreement, and evaluation need serious reconsideration. Some researchers now propose to minimize disagreement and to fix datasets. We argue that this is a gross oversimplification, and likely to conceal the underlying complexity. Instead, we suggest that we need to better capture the sources of disagreement to improve today’s evaluation practice. We discuss three sources of disagreement: from the annotator, the data, and the context, and show how this affects even seemingly objective tasks. Datasets with multiple annotations are becoming more common, as are methods to integrate disagreement into modeling. The logical next step is to extend this to evaluation.

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Patterns of Polysemy and Homonymy in Contextualised Language Models
Janosch Haber | Massimo Poesio
Findings of the Association for Computational Linguistics: EMNLP 2021

One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts. In this paper we investigate the extent to which the contextualised embeddings of word forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy. To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning. Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy. However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation. Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings. Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns. When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.

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Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
Sopan Khosla | Ramesh Manuvinakurike | Vincent Ng | Massimo Poesio | Michael Strube | Carolyn Rosé
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

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The CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
Sopan Khosla | Juntao Yu | Ramesh Manuvinakurike | Vincent Ng | Massimo Poesio | Michael Strube | Carolyn Rosé
Proceedings of the CODI-CRAC 2021 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue

In this paper, we provide an overview of the CODI-CRAC 2021 Shared-Task: Anaphora Resolution in Dialogue. The shared task focuses on detecting anaphoric relations in different genres of conversations. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple subtasks focusing individually on these key relations. We discuss the evaluation scripts used to assess the system performance on these subtasks, and provide a brief summary of the participating systems and the results obtained across ?? runs from 5 teams, with most submissions achieving significantly better results than our baseline methods.

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BERTective: Language Models and Contextual Information for Deception Detection
Tommaso Fornaciari | Federico Bianchi | Massimo Poesio | Dirk Hovy
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts’ preceding context is not considered. This is a severe limitation, as any communication takes place in context, not in a vacuum, and context can help to detect deception. We study a corpus of Italian dialogues containing deceptive statements and implement deep neural models that incorporate various linguistic contexts. We establish a new state-of-the-art identifying deception and find that not all context is equally useful to the task. Only the texts closest to the target, if from the same speaker (rather than questions by an interlocutor), boost performance. We also find that the semantic information in language models such as BERT contributes to the performance. However, BERT alone does not capture the implicit knowledge of deception cues: its contribution is conditional on the concurrent use of attention to learn cues from BERT’s representations.

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SemEval-2021 Task 12: Learning with Disagreements
Alexandra Uma | Tommaso Fornaciari | Anca Dumitrache | Tristan Miller | Jon Chamberlain | Barbara Plank | Edwin Simpson | Massimo Poesio
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Disagreement between coders is ubiquitous in virtually all datasets annotated with human judgements in both natural language processing and computer vision. However, most supervised machine learning methods assume that a single preferred interpretation exists for each item, which is at best an idealization. The aim of the SemEval-2021 shared task on learning with disagreements (Le-Wi-Di) was to provide a unified testing framework for methods for learning from data containing multiple and possibly contradictory annotations covering the best-known datasets containing information about disagreements for interpreting language and classifying images. In this paper we describe the shared task and its results.

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Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
Maciej Ogrodniczuk | Sameer Pradhan | Massimo Poesio | Yulia Grishina | Vincent Ng
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

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Coreference Resolution for the Biomedical Domain: A Survey
Pengcheng Lu | Massimo Poesio
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

Issues with coreference resolution are one of the most frequently mentioned challenges for information extraction from the biomedical literature. Thus, the biomedical genre has long been the second most researched genre for coreference resolution after the news domain, and the subject of a great deal of research for NLP in general. In recent years this interest has grown enormously leading to the development of a number of substantial datasets, of domain-specific contextual language models, and of several architectures. In this paper we review the state of-the-art of coreference in the biomedical domain with a particular attention on these most recent developments.

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Data Augmentation Methods for Anaphoric Zero Pronouns
Abdulrahman Aloraini | Massimo Poesio
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference

In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.

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Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning
Tommaso Fornaciari | Alexandra Uma | Silviu Paun | Barbara Plank | Dirk Hovy | Massimo Poesio
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a novel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft-labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. We measure the divergence between the predictions and the target soft-labels with several loss-functions and evaluate the models on various NLP tasks. We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities, and thereby mitigates overfitting. It significantly improves performance across tasks, beyond the standard approach and prior work.

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Stay Together: A System for Single and Split-antecedent Anaphora Resolution
Juntao Yu | Nafise Sadat Moosavi | Silviu Paun | Massimo Poesio
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The state-of-the-art on basic, single-antecedent anaphora has greatly improved in recent years. Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in “Time-Warner is considering a legal challenge to Telecommunications Inc’s plan to buy half of Showtime Networks Inc–a move that could lead to all-out war between the two powerful companies”. Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available. These systems also focus on split-antecedent anaphors only. In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions. We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics.

2020

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Named Entity Recognition as Dependency Parsing
Juntao Yu | Bernd Bohnet | Massimo Poesio
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points.

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The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task
Abdulrahman Aloraini | Massimo Poesio | Ayman Alhelbawy
Proceedings of the Fifth Arabic Natural Language Processing Workshop

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art.

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Polygloss - A conversational agent for language practice
Etiene da Cruz Dalcol | Massimo Poesio
Proceedings of the 9th Workshop on NLP for Computer Assisted Language Learning

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Multitask Learning-Based Neural Bridging Reference Resolution
Juntao Yu | Massimo Poesio
Proceedings of the 28th International Conference on Computational Linguistics

We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..

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Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution
Juntao Yu | Nafise Sadat Moosavi | Silviu Paun | Massimo Poesio
Proceedings of the 28th International Conference on Computational Linguistics

Now that the performance of coreference resolvers on the simpler forms of anaphoric reference has greatly improved, more attention is devoted to more complex aspects of anaphora. One limitation of virtually all coreference resolution models is the focus on single-antecedent anaphors. Plural anaphors with multiple antecedents-so-called split-antecedent anaphors (as in John met Mary. They went to the movies) have not been widely studied, because they are not annotated in ONTONOTES and are relatively infrequent in other corpora. In this paper, we introduce the first model for unrestricted resolution of split-antecedent anaphors. We start with a strong baseline enhanced by BERT embeddings, and show that we can substantially improve its performance by addressing the sparsity issue. To do this, we experiment with auxiliary corpora where split-antecedent anaphors were annotated by the crowd, and with transfer learning models using element-of bridging references and single-antecedent coreference as auxiliary tasks. Evaluation on the gold annotated ARRAU corpus shows that the out best model uses a combination of three auxiliary corpora achieved F1 scores of 70% and 43.6% when evaluated in a lenient and strict setting, respectively, i.e., 11 and 21 percentage points gain when compared with our baseline.

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Aggregation Driven Progression System for GWAPs
Osman Doruk Kicikoglu | Richard Bartle | Jon Chamberlain | Silviu Paun | Massimo Poesio
Workshop on Games and Natural Language Processing

As the uses of Games-With-A-Purpose (GWAPs) broadens, the systems that incorporate its usages have expanded in complexity. The types of annotations required within the NLP paradigm set such an example, where tasks can involve varying complexity of annotations. Assigning more complex tasks to more skilled players through a progression mechanism can achieve higher accuracy in the collected data while acting as a motivating factor that rewards the more skilled players. In this paper, we present the progression technique implemented in Wormingo , an NLP GWAP that currently includes two layers of task complexity. For the experiment, we have implemented four different progression scenarios on 192 players and compared the accuracy and engagement achieved with each scenario.

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Word Sense Distance in Human Similarity Judgements and Contextualised Word Embeddings
Janosch Haber | Massimo Poesio
Proceedings of the Probability and Meaning Conference (PaM 2020)

Homonymy is often used to showcase one of the advantages of context-sensitive word embedding techniques such as ELMo and BERT. In this paper we want to shift the focus to the related but less exhaustively explored phenomenon of polysemy, where a word expresses various distinct but related senses in different contexts. Specifically, we aim to i) investigate a recent model of polyseme sense clustering proposed by Ortega-Andres & Vicente (2019) through analysing empirical evidence of word sense grouping in human similarity judgements, ii) extend the evaluation of context-sensitive word embedding systems by examining whether they encode differences in word sense similarity and iii) compare the word sense similarities of both methods to assess their correlation and gain some intuition as to how well contextualised word embeddings could be used as surrogate word sense similarity judgements in linguistic experiments.

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Anaphoric Zero Pronoun Identification: A Multilingual Approach
Abdulrahman Aloraini | Massimo Poesio
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

Pro-drop languages such as Arabic, Chinese, Italian or Japanese allow morphologically null but referential arguments in certain syntactic positions, called anaphoric zero-pronouns. Much NLP work on anaphoric zero-pronouns (AZP) is based on gold mentions, but models for their identification are a fundamental prerequisite for their resolution in real-life applications. Such identification requires complex language understanding and knowledge of real-world entities. Transfer learning models, such as BERT, have recently shown to learn surface, syntactic, and semantic information,which can be very useful in recognizing AZPs. We propose a BERT-based multilingual model for AZP identification from predicted zero pronoun positions, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, this is the first neural network model of AZP identification for Arabic; and our approach outperforms the stateof-the-art for Chinese. Experiment results suggest that BERT implicitly encode information about AZPs through their surrounding context.

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Neural Coreference Resolution for Arabic
Abdulrahman Aloraini | Juntao Yu | Massimo Poesio
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

No neural coreference resolver for Arabic exists, in fact we are not aware of any learning-based coreference resolver for Arabic since (Björkelund and Kuhn, 2014). In this paper, we introduce a coreference resolution system for Arabic based on Lee et al’s end-to-end architecture combined with the Arabic version of bert and an external mention detector. As far as we know, this is the first neural coreference resolution system aimed specifically to Arabic, and it substantially outperforms the existing state-of-the-art on OntoNotes 5.0 with a gain of 15.2 points conll F1. We also discuss the current limitations of the task for Arabic and possible approaches that can tackle these challenges.

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Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance
Janosch Haber | Massimo Poesio
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Co-predication is one of the most frequently used linguistic tests to tell apart shifts in polysemic sense from changes in homonymic meaning. It is increasingly coming under criticism as evidence is accumulating that it tends to mis-classify specific cases of polysemic sense alteration as homonymy. In this paper, we collect empirical data to investigate these accusations. We asses how co-predication acceptability relates to explicit ratings of polyseme word sense similarity, and how well either measure can be predicted through the distance between target words’ contextualised word embeddings. We find that sense similarity appears to be a major contributor in determining co-predication acceptability, but that co-predication judgements tend to rate especially less similar sense interpretations equally as unacceptable as homonym pairs, effectively mis-classifying these instances. The tested contextualised word embeddings fail to predict word sense similarity consistently, but the similarities between BERT embeddings show a significant correlation with co-predication ratings. We take this finding as evidence that BERT embeddings might be better representations of context than encodings of word meaning.

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Speaking Outside the Box: Exploring the Benefits of Unconstrained Input in Crowdsourcing and Citizen Science Platforms
Jon Chamberlain | Udo Kruschwitz | Massimo Poesio
Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development"

Crowdsourcing approaches provide a difficult design challenge for developers. There is a trade-off between the efficiency of the task to be done and the reward given to the user for participating, whether it be altruism, social enhancement, entertainment or money. This paper explores how crowdsourcing and citizen science systems collect data and complete tasks, illustrated by a case study from the online language game-with-a-purpose Phrase Detectives. The game was originally developed to be a constrained interface to prevent player collusion, but subsequently benefited from posthoc analysis of over 76k unconstrained inputs from users. Understanding the interface design and task deconstruction are critical for enabling users to participate in such systems and the paper concludes with a discussion of the idea that social networks can be viewed as form of citizen science platform with both constrained and unconstrained inputs making for a highly complex dataset.

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Neural Mention Detection
Juntao Yu | Bernd Bohnet | Massimo Poesio
Proceedings of the 12th Language Resources and Evaluation Conference

Mention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle the full range of mentions. In this work, we propose and compare three neural network-based approaches to mention detection. The first approach is based on the mention detection part of a state of the art coreference resolution system; the second uses ELMO embeddings together with a bidirectional LSTM and a biaffine classifier; the third approach uses the recently introduced BERT model. Our best model (using a biaffine classifier) achieves gains of up to 1.8 percentage points on mention recall when compared with a strong baseline in a HIGH RECALL coreference annotation setting. The same model achieves improvements of up to 5.3 and 6.2 p.p. when compared with the best-reported mention detection F1 on the CONLL and CRAC coreference data sets respectively in a HIGH F1 annotation setting. We then evaluate our models for coreference resolution by using mentions predicted by our best model in start-of-the-art coreference systems. The enhanced model achieved absolute improvements of up to 1.7 and 0.7 p.p. when compared with our strong baseline systems (pipeline system and end-to-end system) respectively. For nested NER, the evaluation of our model on the GENIA corpora shows that our model matches or outperforms state-of-the-art models despite not being specifically designed for this task.

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A Cluster Ranking Model for Full Anaphora Resolution
Juntao Yu | Alexandra Uma | Massimo Poesio
Proceedings of the 12th Language Resources and Evaluation Conference

Anaphora resolution (coreference) systems designed for the CONLL 2012 dataset typically cannot handle key aspects of the full anaphora resolution task such as the identification of singletons and of certain types of non-referring expressions (e.g., expletives), as these aspects are not annotated in that corpus. However, the recently released dataset for the CRAC 2018 Shared Task can now be used for that purpose. In this paper, we introduce an architecture to simultaneously identify non-referring expressions (including expletives, predicative s, and other types) and build coreference chains, including singletons. Our cluster-ranking system uses an attention mechanism to determine the relative importance of the mentions in the same cluster. Additional classifiers are used to identify singletons and non-referring markables. Our contributions are as follows. First all, we report the first result on the CRAC data using system mentions; our result is 5.8% better than the shared task baseline system, which used gold mentions. Second, we demonstrate that the availability of singleton clusters and non-referring expressions can lead to substantially improved performance on non-singleton clusters as well. Third, we show that despite our model not being designed specifically for the CONLL data, it achieves a score equivalent to that of the state-of-the-art system by Kantor and Globerson (2019) on that dataset.

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Cross-lingual Zero Pronoun Resolution
Abdulrahman Aloraini | Massimo Poesio
Proceedings of the 12th Language Resources and Evaluation Conference

In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certain syntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifying and resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavily on semantic coherence and lexical relationships. We propose a BERT-based cross-lingual model for zero pronoun resolution, and evaluate it on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolution for Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction and fine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating which layer encodes the most suitable representation for the task.

2019

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Crowdsourcing and Aggregating Nested Markable Annotations
Chris Madge | Juntao Yu | Jon Chamberlain | Udo Kruschwitz | Silviu Paun | Massimo Poesio
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand–which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.

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Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection
Nafise Sadat Moosavi | Leo Born | Massimo Poesio | Michael Strube
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans.

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A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Massimo Poesio | Jon Chamberlain | Silviu Paun | Juntao Yu | Alexandra Uma | Udo Kruschwitz
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a corpus of anaphoric information (coreference) crowdsourced through a game-with-a-purpose. The corpus, containing annotations for about 108,000 markables, is one of the largest corpora for coreference for English, and one of the largest crowdsourced NLP corpora, but its main feature is the large number of judgments per markable: 20 on average, and over 2.2M in total. This characteristic makes the corpus a unique resource for the study of disagreements on anaphoric interpretation. A second distinctive feature is its rich annotation scheme, covering singletons, expletives, and split-antecedent plurals. Finally, the corpus also comes with labels inferred using a recently proposed probabilistic model of annotation for coreference. The labels are of high quality and make it possible to successfully train a state of the art coreference resolver, including training on singletons and non-referring expressions. The annotation model can also result in more than one label, or no label, being proposed for a markable, thus serving as a baseline method for automatically identifying ambiguous markables. A preliminary analysis of the results is presented.

2018

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A Probabilistic Annotation Model for Crowdsourcing Coreference
Silviu Paun | Jon Chamberlain | Udo Kruschwitz | Juntao Yu | Massimo Poesio
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

The availability of large scale annotated corpora for coreference is essential to the development of the field. However, creating resources at the required scale via expert annotation would be too expensive. Crowdsourcing has been proposed as an alternative; but this approach has not been widely used for coreference. This paper addresses one crucial hurdle on the way to make this possible, by introducing a new model of annotation for aggregating crowdsourced anaphoric annotations. The model is evaluated along three dimensions: the accuracy of the inferred mention pairs, the quality of the post-hoc constructed silver chains, and the viability of using the silver chains as an alternative to the expert-annotated chains in training a state of the art coreference system. The results suggest that our model can extract from crowdsourced annotations coreference chains of comparable quality to those obtained with expert annotation.

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Comparing Bayesian Models of Annotation
Silviu Paun | Bob Carpenter | Jon Chamberlain | Dirk Hovy | Udo Kruschwitz | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 6

The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.

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Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference
Massimo Poesio | Vincent Ng | Maciej Ogrodniczuk
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

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Anaphora Resolution with the ARRAU Corpus
Massimo Poesio | Yulia Grishina | Varada Kolhatkar | Nafise Moosavi | Ina Roesiger | Adam Roussel | Fabian Simonjetz | Alexandra Uma | Olga Uryupina | Juntao Yu | Heike Zinsmeister
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including non-referring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task–identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.

2017

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Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns
Andrew J. Anderson | Douwe Kiela | Stephen Clark | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 5

Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most concrete nouns; however, accuracy is significantly greater using the text-based models for the most abstract nouns. More generally this confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.

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Incongruent Headlines: Yet Another Way to Mislead Your Readers
Sophie Chesney | Maria Liakata | Massimo Poesio | Matthew Purver
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur. We emphasise that this phenomenon should be considered separately from recognised problematic headline types such as clickbait and sensationalism, arguing that existing natural language processing (NLP) methods applied to these related concepts are not appropriate for the automatic detection of headline incongruence, as an analysis beyond stylistic traits is necessary. We therefore suggest a number of alternative methodologies that may be appropriate to the task at hand as a foundation for future work in this area. In addition, we provide an analysis of existing data sets which are related to this work, and motivate the need for a novel data set in this domain.

2016

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Coreference Resolution for the Basque Language with BART
Ander Soraluze | Olatz Arregi | Xabier Arregi | Arantza Díaz de Ilarraza | Mijail Kabadjov | Massimo Poesio
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)

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Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters
Fabio Celli | Evgeny Stepanov | Massimo Poesio | Giuseppe Riccardi
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome.

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The OnForumS corpus from the Shared Task on Online Forum Summarisation at MultiLing 2015
Mijail Kabadjov | Udo Kruschwitz | Massimo Poesio | Josef Steinberger | Jorge Valderrama | Hugo Zaragoza
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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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Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.
Jon Chamberlain | Massimo Poesio | Udo Kruschwitz
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.

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ARRAU: Linguistically-Motivated Annotation of Anaphoric Descriptions
Olga Uryupina | Ron Artstein | Antonella Bristot | Federica Cavicchio | Kepa Rodriguez | Massimo Poesio
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

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.

2015

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Combining Minimally-supervised Methods for Arabic Named Entity Recognition
Maha Althobaiti | Udo Kruschwitz | Massimo Poesio
Transactions of the Association for Computational Linguistics, Volume 3

Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.

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MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations
George Giannakopoulos | Jeff Kubina | John Conroy | Josef Steinberger | Benoit Favre | Mijail Kabadjov | Udo Kruschwitz | Massimo Poesio
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2014

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AraNLP: a Java-based Library for the Processing of Arabic Text.
Maha Althobaiti | Udo Kruschwitz | Massimo Poesio
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present a free, Java-based library named “AraNLP” that covers various Arabic text preprocessing tools. Although a good number of tools for processing Arabic text already exist, integration and compatibility problems continually occur. AraNLP is an attempt to gather most of the vital Arabic text preprocessing tools into one library that can be accessed easily by integrating or accurately adapting existing tools and by developing new ones when required. The library includes a sentence detector, tokenizer, light stemmer, root stemmer, part-of speech tagger (POS-tagger), word segmenter, normalizer, and a punctuation and diacritic remover.

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Identifying fake Amazon reviews as learning from crowds
Tommaso Fornaciari | Massimo Poesio
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Automatic Creation of Arabic Named Entity Annotated Corpus Using Wikipedia
Maha Althobaiti | Udo Kruschwitz | Massimo Poesio
Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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Adapting a State-of-the-art Anaphora Resolution System for Resource-poor Language
Utpal Sikdar | Asif Ekbal | Sriparna Saha | Olga Uryupina | Massimo Poesio
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Of Words, Eyes and Brains: Correlating Image-Based Distributional Semantic Models with Neural Representations of Concepts
Andrew J. Anderson | Elia Bruni | Ulisse Bordignon | Massimo Poesio | Marco Baroni
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Hinrich Schuetze | Pascale Fung | Massimo Poesio
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Hinrich Schuetze | Pascale Fung | Massimo Poesio
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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A Semi-supervised Learning Approach to Arabic Named Entity Recognition
Maha Althobaiti | Udo Kruschwitz | Massimo Poesio
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2012

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Relational Structures and Models for Coreference Resolution
Truc-Vien T. Nguyen | Massimo Poesio
Proceedings of COLING 2012: Posters

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DeCour: a corpus of DEceptive statements in Italian COURts
Tommaso Fornaciari | Massimo Poesio
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In criminal proceedings, sometimes it is not easy to evaluate the sincerity of oral testimonies. DECOUR - DEception in COURt corpus - has been built with the aim of training models suitable to discriminate, from a stylometric point of view, between sincere and deceptive statements. DECOUR is a collection of hearings held in four Italian Courts, in which the speakers lie in front of the judge. These hearings become the object of a specific criminal proceeding for calumny or false testimony, in which the deceptiveness of the statements of the defendant is ascertained. Thanks to the final Court judgment, that points out which lies are told, each utterance of the corpus has been annotated as true, uncertain or false, according to its degree of truthfulness. Since the judgment of deceptiveness follows a judicial inquiry, the annotation has been realized with a greater degree of confidence than ever before. Moreover, in Italy this is the first corpus of deceptive texts not relying on ‘mock' lies created in laboratory conditions, but which has been collected in a natural environment.

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Domain-specific vs. Uniform Modeling for Coreference Resolution
Olga Uryupina | Massimo Poesio
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Several corpora annotated for coreference have been made available in the past decade. These resources differ with respect to their size and the underlying structure: the number of domains and their similarity. Our study compares domain-specific models, learned from small heterogeneous subsets of the investigated corpora, against uniform models, that utilize all the available data. We show that for knowledge-poor baseline systems, domain-specific and uniform modeling yield same results. Systems, relying on large amounts of linguistic knowledge, however, exhibit differences in their performance: with all the designed features in use, domain-specific models suffer from over-fitting, whereas with pre-selected feature sets they tend to outperform union models.

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On the Use of Homogenous Sets of Subjects in Deceptive Language Analysis
Tommaso Fornaciari | Massimo Poesio
Proceedings of the Workshop on Computational Approaches to Deception Detection

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Annotating Archaeological Texts: An Example of Domain-Specific Annotation in the Humanities
Francesca Bonin | Fabio Cavulli | Aronne Noriller | Massimo Poesio | Egon W. Stemle
Proceedings of the Sixth Linguistic Annotation Workshop

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BART goes multilingual: The UniTN / Essex submission to the CoNLL-2012 Shared Task
Olga Uryupina | Alessandro Moschitti | Massimo Poesio
Joint Conference on EMNLP and CoNLL - Shared Task

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On discriminating fMRI representations of abstract WordNet taxonomic categories
Andrew Anderson | Tao Yuan | Brian Murphy | Massimo Poesio
Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon

2011

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Single and multi-objective optimization for feature selection in anaphora resolution
Sriparna Saha | Asif Ekbal | Olga Uryupina | Massimo Poesio
Proceedings of 5th International Joint Conference on Natural Language Processing

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A Cross-Lingual ILP Solution to Zero Anaphora Resolution
Ryu Iida | Massimo Poesio
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Structure-Preserving Pipelines for Digital Libraries
Massimo Poesio | Eduard Barbu | Egon Stemle | Christian Girardi
Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Multi-metric optimization for coreference: The UniTN / IITP / Essex submission to the 2011 CONLL Shared Task
Olga Uryupina | Sriparna Saha | Asif Ekbal | Massimo Poesio
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

2010

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SemEval-2010 Task 1: Coreference Resolution in Multiple Languages
Marta Recasens | Lluís Màrquez | Emili Sapena | M. Antònia Martí | Mariona Taulé | Véronique Hoste | Massimo Poesio | Yannick Versley
Proceedings of the 5th International Workshop on Semantic Evaluation

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BART: A Multilingual Anaphora Resolution System
Samuel Broscheit | Massimo Poesio | Simone Paolo Ponzetto | Kepa Joseba Rodriguez | Lorenza Romano | Olga Uryupina | Yannick Versley | Roberto Zanoli
Proceedings of the 5th International Workshop on Semantic Evaluation

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Detecting Semantic Category in Simultaneous EEG/MEG Recordings
Brian Murphy | Massimo Poesio
Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics

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Proceedings of the Fourth Linguistic Annotation Workshop
Nianwen Xue | Massimo Poesio
Proceedings of the Fourth Linguistic Annotation Workshop

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Anaphoric Annotation of Wikipedia and Blogs in the Live Memories Corpus
Kepa Joseba Rodríguez | Francesca Delogu | Yannick Versley | Egon W. Stemle | Massimo Poesio
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

The Live Memories corpus is an Italian corpus annotated for anaphoric relations. This annotation effort aims to contribute to two significant issues for the CL research: the lack of annotated anaphoric resources for Italian and the increasing interest for the social Web. The Live Memories Corpus contains texts from the Italian Wikipedia about the region Trentino/Süd Tirol and from blog sites with users' comments. It is planned to add a set of articles of local news papers. The corpus includes manual annotated information about morphosyntactic agreement, anaphoricity, and semantic class of the NPs. The anaphoric annotation includes discourse deixis, bridging relations and markes cases of ambiguity with the annotation of alternative interpretations. For the annotation of the anaphoric links the corpus takes into account specific phenomena of the Italian language like incorporated clitics and phonetically non realized pronouns. Reliability studies for the annotation of the mentioned phenomena and for annotation of anaphoric links in general offer satisfactory results. The Wikipedia and blogs dataset will be distributed under Creative Commons Attributions licence.

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BabyExp: Constructing a Huge Multimodal Resource to Acquire Commonsense Knowledge Like Children Do
Massimo Poesio | Marco Baroni | Oswald Lanz | Alessandro Lenci | Alexandros Potamianos | Hinrich Schütze | Sabine Schulte im Walde | Luca Surian
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

There is by now widespread agreement that the most realistic way to construct the large-scale commonsense knowledge repositories required by natural language and artificial intelligence applications is by letting machines learn such knowledge from large quantities of data, like humans do. A lot of attention has consequently been paid to the development of increasingly sophisticated machine learning algorithms for knowledge extraction. However, the nature of the input that humans are exposed to while learning commonsense knowledge has received much less attention. The BabyExp project is collecting very dense audio and video recordings of the first 3 years of life of a baby. The corpus constructed in this way will be transcribed with automated techniques and made available to the research community. Moreover, techniques to extract commonsense conceptual knowledge incrementally from these multimodal data are also being explored within the project. The current paper describes BabyExp in general, and presents pilot studies on the feasibility of the automated audio and video transcriptions.

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Extending BART to Provide a Coreference Resolution System for German
Samuel Broscheit | Simone Paolo Ponzetto | Yannick Versley | Massimo Poesio
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present a flexible toolkit-based approach to automatic coreference resolution on German text. We start with our previous work aimed at reimplementing the system from Soon et al. (2001) for English, and extend it to duplicate a version of the state-of-the-art proposal from Klenner and Ailloud (2009). Evaluation performed on a benchmarking dataset, namely the TueBa-D/Z corpus (Hinrichs et al., 2005b), shows that machine learning based coreference resolution can be robustly performed in a language other than English.

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Creating a Coreference Resolution System for Italian
Massimo Poesio | Olga Uryupina | Yannick Versley
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper summarizes our work on creating a full-scale coreference resolution (CR) system for Italian, using BART ― an open-source modular CR toolkit initially developed for English corpora. We discuss our experiments on language-specific issues of the task. As our evaluation experiments show, a language-agnostic system (designed primarily for English) can achieve a performance level in high forties (MUC F-score) when re-trained and tested on a new language, at least on gold mention boundaries. Compared to this level, we can improve our F-score by around 10% introducing a small number of language-specific changes. This shows that, with a modular coreference resolution platform, such as BART, one can straightforwardly develop a family of robust and reliable systems for various languages. We hope that our experiments will encourage researchers working on coreference in other languages to create their own full-scale coreference resolution systems ― as we have mentioned above, at the moment such modules exist only for very few languages other than English.

2009

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Constructing an Anaphorically Annotated Corpus with Non-Experts: Assessing the Quality of Collaborative Annotations
Jon Chamberlain | Udo Kruschwitz | Massimo Poesio
Proceedings of the 2009 Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources (People’s Web)

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Play your way to an annotated corpus: Games with a purpose and anaphoric annotation
Massimo Poesio
Proceedings of the Eight International Conference on Computational Semantics

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Interactive Gesture in Dialogue: a PTT Model
Hannes Rieser | Massimo Poesio
Proceedings of the SIGDIAL 2009 Conference

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Unsupervised Knowledge Extraction for Taxonomies of Concepts from Wikipedia
Eduard Barbu | Massimo Poesio
Proceedings of the International Conference RANLP-2009

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EEG responds to conceptual stimuli and corpus semantics
Brian Murphy | Marco Baroni | Massimo Poesio
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Evaluating Centering for Information Ordering Using Corpora
Nikiforos Karamanis | Chris Mellish | Massimo Poesio | Jon Oberlander
Computational Linguistics, Volume 35, Number 1, March 2009

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Obituaries: Janet Hitzeman
Massimo Poesio | David Day | Inderjeet Mani
Computational Linguistics, Volume 35, Number 4, December 2009

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State-of-the-art NLP Approaches to Coreference Resolution: Theory and Practical Recipes
Simone Paolo Ponzetto | Massimo Poesio
Tutorial Abstracts of ACL-IJCNLP 2009

2008

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Survey Article: Inter-Coder Agreement for Computational Linguistics
Ron Artstein | Massimo Poesio
Computational Linguistics, Volume 34, Number 4, December 2008

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BART: A Modular Toolkit for Coreference Resolution
Yannick Versley | Simone Paolo Ponzetto | Massimo Poesio | Vladimir Eidelman | Alan Jern | Jason Smith | Xiaofeng Yang | Alessandro Moschitti
Proceedings of the ACL-08: HLT Demo Session

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A Corpus for Cross-Document Co-reference
David Day | Janet Hitzeman | Michael Wick | Keith Crouch | Massimo Poesio
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes a newly created text corpus of news articles that has been annotated for cross-document co-reference. Being able to robustly resolve references to entities across document boundaries will provide a useful capability for a variety of tasks, ranging from practical information retrieval applications to challenging research in information extraction and natural language understanding. This annotated corpus is intended to encourage the development of systems that can more accurately address this problem. A manual annotation tool was developed that allowed the complete corpus to be searched for likely co-referring entity mentions. This corpus of 257K words links mentions of co-referent people, locations and organizations (subject to some additional constraints). Each of the documents had already been annotated for within-document co-reference by the LDC as part of the ACE series of evaluations. The annotation process was bootstrapped with a string-matching-based linking procedure, and we report on some of initial experimentation with the data. The cross-document linking information will be made publicly available.

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Anaphoric Annotation in the ARRAU Corpus
Massimo Poesio | Ron Artstein
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.

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BART: A modular toolkit for coreference resolution
Yannick Versley | Simone Ponzetto | Massimo Poesio | Vladimir Eidelman | Alan Jern | Jason Smith | Xiaofeng Yang | Alessandro Moschitti
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort. Accordingly, there is very limited availability of off-the shelf tools for researchers whose interests are not primarily in coreference or others who want to concentrate on a specific aspect of the problem. We present BART, a highly modular toolkit for developing coreference applications. In the Johns Hopkins workshop on using lexical and encyclopedic knowledge for entity disambiguation, the toolkit was used to extend a reimplementation of Soon et al.’s proposal with a variety of additional syntactic and knowledge-based features, and experiment with alternative resolution processes, preprocessing tools, and classifiers. BART has been released as open source software and is available from http://www.sfs.uni-tuebingen.de/~versley/BART

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ANAWIKI: Creating Anaphorically Annotated Resources through Web Cooperation
Massimo Poesio | Udo Kruschwitz | Jon Chamberlain
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The ability to make progress in Computational Linguistics depends on the availability of large annotated corpora, but creating such corpora by hand annotation is very expensive and time consuming; in practice, it is unfeasible to think of annotating more than one million words. However, the success of Wikipedia and other projects shows that another approach might be possible: take advantage of the willingness of Web users to contribute to collaborative resource creation. AnaWiki is a recently started project that will develop tools to allow and encourage large numbers of volunteers over the Web to collaborate in the creation of semantically annotated corpora (in the first instance, of a corpus annotated with information about anaphora).

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Addressing the Resource Bottleneck to Create Large-Scale Annotated Texts
Jon Chamberlain | Massimo Poesio | Udo Kruschwitz
Semantics in Text Processing. STEP 2008 Conference Proceedings

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Coreference Systems Based on Kernels Methods
Yannick Versley | Alessandro Moschitti | Massimo Poesio | Xiaofeng Yang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Discovering contradicting protein-protein interactions in text
Olivia Sanchez | Massimo Poesio
Biological, translational, and clinical language processing

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Standoff Coordination for Multi-Tool Annotation in a Dialogue Corpus
Kepa Joseba Rodríguez | Stefanie Dipper | Michael Götze | Massimo Poesio | Giuseppe Riccardi | Christian Raymond | Joanna Rabiega-Wiśniewska
Proceedings of the Linguistic Annotation Workshop

2006

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An Anaphora Resolution-Based Anonymization Module
M. Poesio | M. A. Kabadjov | P. Goux | U. Kruschwitz | E. Bishop | L. Corti
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Growing privacy and security concerns mean there is an increasing need for data to be anonymized before being publically released. We present a module for anonymizing references implemented as part of the SQUAD tools for specifying and testing non-proprietary means of storing and marking-up data using universal (XML) standards and technologies. The tool is implemented on top of the GUITAR anaphoric resolver.

2005

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Merging PropBank, NomBank, TimeBank, Penn Discourse Treebank and Coreference
James Pustejovsky | Adam Meyers | Martha Palmer | Massimo Poesio
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

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The Reliability of Anaphoric Annotation, Reconsidered: Taking Ambiguity into Account
Massimo Poesio | Ron Artstein
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

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Identifying Concept Attributes Using a Classifier
Massimo Poesio | Abdulrahman Almuhareb
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

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Improving LSA-based Summarization with Anaphora Resolution
Josef Steinberger | Mijail Kabadjov | Massimo Poesio | Olivia Sanchez-Graillet
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Acquiring Bayesian Networks from Text
Olivia Sanchez-Graillet | Massimo Poesio
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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A General-Purpose, Off-the-shelf Anaphora Resolution Module: Implementation and Preliminary Evaluation
Massimo Poesio | Mijail A. Kabadjov
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Discourse Annotation and Semantic Annotation in the GNOME corpus
Massimo Poesio
Proceedings of the Workshop on Discourse Annotation

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Discourse-New Detectors for Definite Description Resolution: A Survey and a Preliminary Proposal
Massimo Poesio | Olga Uryupina | Renata Vieira | Mijail Alexandrov-Kabadjov | Rodrigo Goulart
Proceedings of the Conference on Reference Resolution and Its Applications

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The MATE/GNOME Proposals for Anaphoric Annotation, Revisited
Massimo Poesio
Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue at HLT-NAACL 2004

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Attribute-Based and Value-Based Clustering: An Evaluation
Abdulrahman Almuhareb | Massimo Poesio
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Identifying Broken Plurals in Unvowelised Arabic Tex
Abduelbaset Goweder | Massimo Poesio | Anne De Roeck | Jeff Reynolds
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Centering: A Parametric Theory and Its Instantiations
Massimo Poesio | Rosemary Stevenson | Barbara Di Eugenio | Janet Hitzeman
Computational Linguistics, Volume 30, Number 3, September 2004

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Learning to Resolve Bridging References
Massimo Poesio | Rahul Mehta | Axel Maroudas | Janet Hitzeman
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Evaluating Centering-Based Metrics of Coherence
Nikiforos Karamanis | Massimo Poesio | Chris Mellish | Jon Oberlander
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

2003

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Associative Descriptions and Salience: A Preliminary Investigation
Massimo Poesio
Proceedings of the 2003 EACL Workshop on The Computational Treatment of Anaphora

2002

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Acquiring Lexical Knowledge for Anaphora Resolution
Massimo Poesio | Tomonori Ishikawa | Sabine Schulte im Walde | Renata Vieira
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

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Corpus-based NP Modifier Generation
Hua Cheng | Massimo Poesio | Renate Henschel | Chris Mellish
Second Meeting of the North American Chapter of the Association for Computational Linguistics

2000

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An Empirically-based System for Processing Definite Descriptions
Renata Vieira | Massimo Poesio
Computational Linguistics, Volume 26, Number 4, December 2000

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Specifying the Parameters of Centering Theory: a Corpus-Based Evaluation using Text from Application-Oriented Domains
M. Poesio | H. Cheng | R. Henschel | J. Hitzeman | R. Kibble | R. Stevenson
Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics

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Modelling Grounding and Discourse Obligations Using Update Rules
Colin Matheson | Massimo Poesio | David Traum
1st Meeting of the North American Chapter of the Association for Computational Linguistics

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Semantic Annotation for Generation: Issues in Annotating a Corpus to Develop and Evaluate Discourse Entity Realization Algorithms
Massimo Poesio
Proceedings of the COLING-2000 Workshop on Semantic Annotation and Intelligent Content

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Pronominalization revisited
Renate Henschel | Hua Cheng | Massimo Poesio
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

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Corpus-based Development and Evaluation of a System for Processing Definite Descriptions
Renata Vieira | Massimo Poesio
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Annotating a Corpus to Develop and Evaluate Discourse Entity Realization Algorithms: Issues and Preliminary Results
Massimo Poesio
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

1999

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The MATE meta-scheme for coreference in dialogues in multiple languages
M. Poesio | F. Bruneseaux | L. Romary
Towards Standards and Tools for Discourse Tagging

1998

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Long Distance Pronominalisation and Global Focus
Janet Hitzeman | Massimo Poesio
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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A Corpus-based Investigation of Definite Description Use
Massimo Poesio | Renata Vieira
Computational Linguistics, Volume 24, Number 2, June 1998

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Long Distance Pronominalisation and Global Focus
Janet Hitzeman | Massimo Poesio
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

1997

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Resolving bridging references in unrestricted text
Massimo Poesio | Renata Vieira | Simone Teufel
Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts

1996

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Book Reviews: Logic and Lexicon
Massimo Poesio
Computational Linguistics, Volume 22, Number 1, March 1996

1993

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Temporal Centering
Megumi Kameyama | Rebecca Passonneau | Massimo Poesio
31st Annual Meeting of the Association for Computational Linguistics

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Assigning a Semantic Scope to Operators
Massimo Poesio
31st Annual Meeting of the Association for Computational Linguistics

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