Lucie Barque


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FrSemCor: Annotating a French Corpus with Supersenses
Lucie Barque | Pauline Haas | Richard Huyghe | Delphine Tribout | Marie Candito | Benoit Crabbé | Vincent Segonne
Proceedings of the 12th Language Resources and Evaluation Conference

French, as many languages, lacks semantically annotated corpus data. Our aim is to provide the linguistic and NLP research communities with a gold standard sense-annotated corpus of French, using WordNet Unique Beginners as semantic tags, thus allowing for interoperability. In this paper, we report on the first phase of the project, which focused on the annotation of common nouns. The resulting dataset consists of more than 12,000 French noun occurrences which were annotated in double blind and adjudicated according to a carefully redefined set of supersenses. The resource is released online under a Creative Commons Licence.

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SLICE: Supersense-based Lightweight Interpretable Contextual Embeddings
Cindy Aloui | Carlos Ramisch | Alexis Nasr | Lucie Barque
Proceedings of the 28th International Conference on Computational Linguistics

Contextualised embeddings such as BERT have become de facto state-of-the-art references in many NLP applications, thanks to their impressive performances. However, their opaqueness makes it hard to interpret their behaviour. SLICE is a hybrid model that combines supersense labels with contextual embeddings. We introduce a weakly supervised method to learn interpretable embeddings from raw corpora and small lists of seed words. Our model is able to represent both a word and its context as embeddings into the same compact space, whose dimensions correspond to interpretable supersenses. We assess the model in a task of supersense tagging for French nouns. The little amount of supervision required makes it particularly well suited for low-resourced scenarios. Thanks to its interpretability, we perform linguistic analyses about the predicted supersenses in terms of input word and context representations.


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Demonette2 - Une base de données dérivationnelle du français à grande échelle : premiers résultats (Demonette2 – A large scale derivational database for French: first results)
Fiammetta Namer | Lucie Barque | Olivier Bonami | Pauline Haas | Nabil Hathout | Delphine Tribout
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts

Cet article présente la conception et le développement de Demonette2, une base de données dérivationnelle à grande échelle du français, développée dans le cadre du projet ANR Démonext (ANR-17-CE23-0005). L’article décrit les objectifs du projet, la structure de la base et expose les premiers résultats du projet, en mettant l’accent sur un enjeu crucial : la question du codage sémantique des entrées et des relations.


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Improvement of VerbNet-like resources by frame typing
Laurence Danlos | Matthieu Constant | Lucie Barque
Proceedings of the Workshop on Grammar and Lexicon: interactions and interfaces (GramLex)

Verbenet is a French lexicon developed by “translation” of its English counterpart — VerbNet (Kipper-Schuler, 2005)—and treatment of the specificities of French syntax (Pradet et al., 2014; Danlos et al., 2016). One difficulty encountered in its development springs from the fact that the list of (potentially numerous) frames has no internal organization. This paper proposes a type system for frames that shows whether two frames are variants of a given alternation. Frame typing facilitates coherence checking of the resource in a “virtuous circle”. We present the principles underlying a program we developed and used to automatically type frames in VerbeNet. We also show that our system is portable to other languages.


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Developing a French FrameNet: Methodology and First results
Marie Candito | Pascal Amsili | Lucie Barque | Farah Benamara | Gaël de Chalendar | Marianne Djemaa | Pauline Haas | Richard Huyghe | Yvette Yannick Mathieu | Philippe Muller | Benoît Sagot | Laure Vieu
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The Asfalda project aims to develop a French corpus with frame-based semantic annotations and automatic tools for shallow semantic analysis. We present the first part of the project: focusing on a set of notional domains, we delimited a subset of English frames, adapted them to French data when necessary, and developed the corresponding French lexicon. We believe that working domain by domain helped us to enforce the coherence of the resulting resource, and also has the advantage that, though the number of frames is limited (around a hundred), we obtain full coverage within a given domain.


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Extracting a Semantic Lexicon of French Adjectives from a Large Lexicographic Dictionary
Selja Seppälä | Lucie Barque | Alexis Nasr
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Dictionary-ontology cross-enrichment
Emmanuel Eckard | Lucie Barque | Alexis Nasr | Benoît Sagot
Proceedings of the 3rd Workshop on Cognitive Aspects of the Lexicon


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Building a Lexicon of French Deverbal Nouns from a Semantically Annotated Corpus
Antonio Balvet | Lucie Barque | Rafael Marín
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper presents project Nomage, which aims at describing the aspectual properties of deverbal nouns in an empirical way. It is centered on the development of two resources: a semantically annotated corpus of deverbal nouns, and an electronic lexicon. They are both presented in this paper, and emphasize how the semantic annotations of the corpus allow the lexicographic description of deverbal nouns to be validated, in particular their polysemy. Nominalizations have occupied a central place in grammatical analysis, with a focus on morphological and syntactic aspects. More recently, researchers have begun to address a specific issue often neglected before, i.e. the semantics of nominalizations, and its implications for Natural Language Processing applications such as electronic ontologies or Information Retrieval. We focus on precisely this issue in the research project NOMAGE, funded by the French National Research Agency (ANR-07-JCJC-0085-01). In this paper, we present the Nomage corpus and the annotations we make on deverbal nouns (section 2). We then show how we build our lexicon with the semantically annotated corpus and illustrate the kind of generalizations we can make from such data (section 3).