Caroline Pasquer


Seen2Unseen at PARSEME Shared Task 2020: All Roads do not Lead to Unseen Verb-Noun VMWEs
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the Joint Workshop on Multiword Expressions and Electronic Lexicons

We describe the Seen2Unseen system that participated in edition 1.2 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). The identification of VMWEs that do not appear in the provided training corpora (called unseen VMWEs) – with a focus here on verb-noun VMWEs – is based on mutual information and lexical substitution or translation of seen VMWEs. We present the architecture of the system, report results for 14 languages, and propose an error analysis.

Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the 28th International Conference on Computational Linguistics

Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.


If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions
Caroline Pasquer | Agata Savary | Carlos Ramisch | Jean-Yves Antoine
Proceedings of the 27th International Conference on Computational Linguistics

Multiword expressions, especially verbal ones (VMWEs), show idiosyncratic variability, which is challenging for NLP applications, hence the need for VMWE identification. We focus on the task of variant identification, i.e. identifying variants of previously seen VMWEs, whatever their surface form. We model the problem as a classification task. Syntactic subtrees with previously seen combinations of lemmas are first extracted, and then classified on the basis of features relevant to morpho-syntactic variation of VMWEs. Feature values are both absolute, i.e. hold for a particular VMWE candidate, and relative, i.e. based on comparing a candidate with previously seen VMWEs. This approach outperforms a baseline by 4 percent points of F-measure on a French corpus.

Towards a Variability Measure for Multiword Expressions
Caroline Pasquer | Agata Savary | Jean-Yves Antoine | Carlos Ramisch
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

One of the most outstanding properties of multiword expressions (MWEs), especially verbal ones (VMWEs), important both in theoretical models and applications, is their idiosyncratic variability. Some MWEs are always continuous, while some others admit certain types of insertions. Components of some MWEs are rarely or never modified, while some others admit either specific or unrestricted modification. This unpredictable variability profile of MWEs hinders modeling and processing them as “words-with-spaces” on the one hand, and as regular syntactic structures on the other hand. Since variability of MWEs is a matter of scale rather than a binary property, we propose a 2-dimensional language-independent measure of variability dedicated to verbal MWEs based on syntactic and discontinuity-related clues. We assess its relevance with respect to a linguistic benchmark and its utility for the tasks of VMWE classification and variant identification on a French corpus.

VarIDE at PARSEME Shared Task 2018: Are Variants Really as Alike as Two Peas in a Pod?
Caroline Pasquer | Carlos Ramisch | Agata Savary | Jean-Yves Antoine
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

We describe the VarIDE system (standing for Variant IDEntification) which participated in the edition 1.1 of the PARSEME shared task on automatic identification of verbal multiword expressions (VMWEs). Our system focuses on the task of VMWE variant identification by using morphosyntactic information in the training data to predict if candidates extracted from the test corpus could be idiomatic, thanks to a naive Bayes classifier. We report results for 19 languages.


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Annotation d’expressions polylexicales verbales en français (Annotation of verbal multiword expressions in French)
Marie Candito | Mathieu Constant | Carlos Ramisch | Agata Savary | Yannick Parmentier | Caroline Pasquer | Jean-Yves Antoine
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

Nous décrivons la partie française des données produites dans le cadre de la campagne multilingue PARSEME sur l’identification d’expressions polylexicales verbales (Savary et al., 2017). Les expressions couvertes pour le français sont les expressions verbales idiomatiques, les verbes intrinsèquement pronominaux et une généralisation des constructions à verbe support. Ces phénomènes ont été annotés sur le corpus French-UD (Nivre et al., 2016) et le corpus Sequoia (Candito & Seddah, 2012), soit un corpus de 22 645 phrases, pour un total de 4 962 expressions annotées. On obtient un ratio d’une expression annotée tous les 100 tokens environ, avec un fort taux d’expressions discontinues (40%).

Expressions polylexicales verbales : étude de la variabilité en corpus (Verbal MWEs : a corpus-based study of variability)
Caroline Pasquer
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. 19es REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL 2017)

La reconnaissance et le traitement approprié des expressions polylexicales (EP) constituent un enjeu pour différentes applications en traitement automatique des langues. Ces expressions sont susceptibles d’apparaître sous d’autres formes que leur forme canonique, d’où l’intérêt d’étudier leur profil de variabilité. Dans cet article, nous proposons de donner un aperçu de motifs de variation syntaxiques et/ou morphologiques d’après un corpus de 4441 expressions polylexicales verbales (EPV) annotées manuellement. L’objectif poursuivi est de générer automatiquement les différentes variantes pour améliorer la performance des techniques de traitement automatique des EPV.