1 code implementation • COLING (TextGraphs) 2022 • Aman Sinha, Sandrine Ollinger, Mathieu Constant
This paper focuses on the task of word sense disambiguation (WSD) on lexicographic examples relying on the French Lexical Network (fr-LN).
no code implementations • SemEval (NAACL) 2022 • Timothee Mickus, Kees Van Deemter, Mathieu Constant, Denis Paperno
Word embeddings have advanced the state of the art in NLP across numerous tasks.
no code implementations • WS (NoDaLiDa) 2019 • Hazem Al Saied, Marie Candito, Mathieu Constant
In this paper, we compare the use of linear versus neural classifiers in a greedy transition system for MWE identification.
no code implementations • JEP/TALN/RECITAL 2021 • Cristina Holgado, Alexei Lavrentiev, Mathieu Constant
Pour les langues historiques non stabilisées comme le français médiéval, la lemmatisation automatique présente toujours des défis, car cette langue connaît une forte variation graphique.
1 code implementation • 23 Jul 2024 • Ioana Buhnila, Aman Sinha, Mathieu Constant
Recent surge in the accessibility of large language models (LLMs) to the general population can lead to untrackable use of such models for medical-related recommendations.
no code implementations • 17 Jul 2024 • Aman Sinha, Timothee Mickus, Marianne Clausel, Mathieu Constant, Xavier Coubez
The success of pretrained language models (PLMs) across a spate of use-cases has led to significant investment from the NLP community towards building domain-specific foundational models.
no code implementations • 15 Sep 2023 • Rohit Agarwal, Aman Sinha, Ayan Vishwakarma, Xavier Coubez, Marianne Clausel, Mathieu Constant, Alexander Horsch, Dilip K. Prasad
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values.
no code implementations • 7 Jun 2022 • Timothee Mickus, Denis Paperno, Mathieu Constant
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm.
1 code implementation • 27 May 2022 • Timothee Mickus, Kees Van Deemter, Mathieu Constant, Denis Paperno
Word embeddings have advanced the state of the art in NLP across numerous tasks.
no code implementations • 17 Aug 2021 • Timothee Mickus, Mathieu Constant, Denis Paperno
Can language models learn grounded representations from text distribution alone?
no code implementations • JEPTALNRECITAL 2020 • Timothee Mickus, Mathieu Constant, Denis Paperno
La g{\'e}n{\'e}ration de d{\'e}finitions est une t{\^a}che r{\'e}cente qui vise {\`a} produire des d{\'e}finitions lexicographiques {\`a} partir de plongements lexicaux.
no code implementations • LREC 2020 • Kar{\"e}n Fort, Bruno Guillaume, Yann-Alan Pilatte, Mathieu Constant, Nicolas Lef{\`e}bvre
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.
no code implementations • WS 2019 • Timothee Mickus, Denis Paperno, Mathieu Constant
Defining words in a textual context is a useful task both for practical purposes and for gaining insight into distributed word representations.
no code implementations • 13 Nov 2019 • Timothee Mickus, Denis Paperno, Mathieu Constant, Kees Van Deemter
Contextualized word embeddings, i. e. vector representations for words in context, are naturally seen as an extension of previous noncontextual distributional semantic models.
no code implementations • WS 2019 • Marine Schmitt, Mathieu Constant
This article focuses on the lemmatization of multiword expressions (MWEs).
no code implementations • JEPTALNRECITAL 2019 • Marine Schmitt, Elise Moreau, Mathieu Constant, Agata Savary
Nous pr{\'e}sentons le d{\'e}monstrateur en-ligne du projet ANR PARSEME-FR d{\'e}di{\'e} aux expressions polylexicales.
no code implementations • CL 2017 • Mathieu Constant, G{\"u}l{\c{s}}en Eryi{\v{g}}it, Johanna Monti, Lonneke van der Plas, Carlos Ramisch, Michael Rosner, Amalia Todirascu
The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs.
no code implementations • JEPTALNRECITAL 2017 • C, Marie ito, Mathieu Constant, Carlos Ramisch, Agata Savary, Yannick Parmentier, Caroline Pasquer, Jean-Yves Antoine
Nous d{\'e}crivons la partie fran{\c{c}}aise des donn{\'e}es produites dans le cadre de la campagne multilingue PARSEME sur l{'}identification d{'}expressions polylexicales verbales (Savary et al., 2017).