Nicolas Lefebvre

Also published as: Nicolas Lefèbvre


2020

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Rigor Mortis: Annotating MWEs with a Gamified Platform
Karën Fort | Bruno Guillaume | Yann-Alan Pilatte | Mathieu Constant | Nicolas Lefèbvre
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present here Rigor Mortis, a gamified crowdsourcing platform designed to evaluate the intuition of the speakers, then train them to annotate multi-word expressions (MWEs) in French corpora. We previously showed that the speakers’ intuition is reasonably good (65% in recall on non-fixed MWE). We detail here the annotation results, after a training phase using some of the tests developed in the PARSEME-FR project.

2018

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“Fingers in the Nose”: Evaluating Speakers’ Identification of Multi-Word Expressions Using a Slightly Gamified Crowdsourcing Platform
Karën Fort | Bruno Guillaume | Matthieu Constant | Nicolas Lefèbvre | Yann-Alan Pilatte
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

This article presents the results we obtained in crowdsourcing French speakers’ intuition concerning multi-work expressions (MWEs). We developed a slightly gamified crowdsourcing platform, part of which is designed to test users’ ability to identify MWEs with no prior training. The participants perform relatively well at the task, with a recall reaching 65% for MWEs that do not behave as function words.

2017

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Vers l’annotation par le jeu de corpus (plus) complexes : le cas de la langue de spécialité (Towards (more) complex corpora annotation using a game with a purpose : the case of scientific language)
Karën Fort | Bruno Guillaume | Nicolas Lefebvre | Laura Ramírez | Mathilde Regnault | Mary Collins | Oksana Gavrilova | Tanti Kristanti
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 - Articles courts

Nous avons précédemment montré qu’il est possible de faire produire des annotations syntaxiques de qualité par des participants à un jeu ayant un but. Nous présentons ici les résultats d’une expérience visant à évaluer leur production sur un corpus plus complexe, en langue de spécialité, en l’occurrence un corpus de textes scientifiques sur l’ADN. Nous déterminons précisément la complexité de ce corpus, puis nous évaluons les annotations en syntaxe de dépendances produites par les joueurs par rapport à une référence mise au point par des experts du domaine.

2016

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Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax
Bruno Guillaume | Karën Fort | Nicolas Lefebvre
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This article presents the results we obtained on a complex annotation task (that of dependency syntax) using a specifically designed Game with a Purpose, ZombiLingo. We show that with suitable mechanisms (decomposition of the task, training of the players and regular control of the annotation quality during the game), it is possible to obtain annotations whose quality is significantly higher than that obtainable with a parser, provided that enough players participate. The source code of the game and the resulting annotated corpora (for French) are freely available.