Kata Gábor


2020

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Emergence of Syntax Needs Minimal Supervision
Raphaël Bailly | Kata Gábor
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper is a theoretical contribution to the debate on the learnability of syntax from a corpus without explicit syntax-specific guidance. Our approach originates in the observable structure of a corpus, which we use to define and isolate grammaticality (syntactic information) and meaning/pragmatics information. We describe the formal characteristics of an autonomous syntax and show that it becomes possible to search for syntax-based lexical categories with a simple optimization process, without any prior hypothesis on the form of the model.

2018

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SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers
Kata Gábor | Davide Buscaldi | Anne-Kathrin Schumann | Behrang QasemiZadeh | Haïfa Zargayouna | Thierry Charnois
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three different subtasks. The subtasks were designed so as to compare and quantify the effect of different pre-processing steps on the relation classification results. We expect the task to be relevant for a broad range of researchers working on extracting specialized knowledge from domain corpora, for example but not limited to scientific or bio-medical information extraction. The task attracted a total of 32 participants, with 158 submissions across different scenarios.

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Apport des dépendances syntaxiques et des patrons séquentiels à l’extraction de relations ()
Kata Gábor | Nadège Lechevrel | Isabelle Tellier | Davide Buscaldi | Haifa Zargayouna | Thierry Charnois
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

2017

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Exploring Vector Spaces for Semantic Relations
Kata Gábor | Haïfa Zargayouna | Isabelle Tellier | Davide Buscaldi | Thierry Charnois
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Word embeddings are used with success for a variety of tasks involving lexical semantic similarities between individual words. Using unsupervised methods and just cosine similarity, encouraging results were obtained for analogical similarities. In this paper, we explore the potential of pre-trained word embeddings to identify generic types of semantic relations in an unsupervised experiment. We propose a new relational similarity measure based on the combination of word2vec’s CBOW input and output vectors which outperforms concurrent vector representations, when used for unsupervised clustering on SemEval 2010 Relation Classification data.

2016

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Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature
Kata Gábor | Haïfa Zargayouna | Davide Buscaldi | Isabelle Tellier | Thierry Charnois
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpora. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.

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Détection et classification non supervisées de relations sémantiques dans des articles scientifiques (Unsupervised Classification of Semantic Relations in Scientific Papers)
Kata Gábor | Isabelle Tellier | Thierry Charnois | Haïfa Zargayouna | Davide Buscaldi
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs)

Dans cet article, nous abordons une tâche encore peu explorée, consistant à extraire automatiquement l’état de l’art d’un domaine scientifique à partir de l’analyse d’articles de ce domaine. Nous la ramenons à deux sous-tâches élémentaires : l’identification de concepts et la reconnaissance de relations entre ces concepts. Une extraction terminologique permet d’identifier les concepts candidats, qui sont ensuite alignés à des ressources externes. Dans un deuxième temps, nous cherchons à reconnaître et classifier automatiquement les relations sémantiques entre concepts de manière nonsupervisée, en nous appuyant sur différentes techniques de clustering et de biclustering. Nous mettons en œuvre ces deux étapes dans un corpus extrait de l’archive de l’ACL Anthology. Une analyse manuelle nous a permis de proposer une typologie des relations sémantiques, et de classifier un échantillon d’instances de relations. Les premières évaluations suggèrent l’intérêt du biclustering pour détecter de nouveaux types de relations dans le corpus.

2014

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Automated Error Detection in Digitized Cultural Heritage Documents
Kata Gábor | Benoît Sagot
Proceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

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The WoDiS System - WOlf and DIStributions for Lexical Substitution (Le système WoDiS - WOLF et DIStributions pour la substitution lexicale) [in French]
Kata Gábor
TALN-RECITAL 2014 Workshop SemDis 2014 : Enjeux actuels de la sémantique distributionnelle (SemDis 2014: Current Challenges in Distributional Semantics)

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Named Entity Recognition and Correction in OCRized Corpora (Détection et correction automatique d’entités nommées dans des corpus OCRisés) [in French]
Benoît Sagot | Kata Gábor
Proceedings of TALN 2014 (Volume 2: Short Papers)

2012

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Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations
Kata Gábor | Marianna Apidianaki | Benoît Sagot | Éric Villemonte de La Clergerie
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Franc¸ais). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy.

2007

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Clustering Hungarian Verbs on the Basis of Complementation Patterns
Kata Gábor | Enikő Héja
Proceedings of the ACL 2007 Student Research Workshop