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Found 10 papers, 2 papers with code
FlauBERT : des mod\`eles de langue contextualis\'es pr\'e-entra\^\in\'es pour le fran\ccais (FlauBERT : Unsupervised Language Model Pre-training for French)
Les mod{\`e}les de langue pr{\'e}-entra{\^\i}n{\'e}s sont d{\'e}sormais indispensables pour obtenir des r{\'e}sultats {\`a} l{'}{\'e}tat-de-l{'}art dans de nombreuses t{\^a}ches du TALN.
Using Wiktionary as a resource for WSD : the case of French verbs
In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French.
Repr\'esentation et analyse automatique des discontinuit\'es syntaxiques dans les corpus arbor\'es en constituants du fran\ccais (Representation and parsing of syntactic discontinuities in French constituent treebanks)
Enfin, nos exp{\'e}riences montrent que la raret{\'e} des ph{\'e}nom{\`e}nes concern{\'e}s dans les donn{\'e}es fran{\c{c}}aises pose des difficult{\'e}s pour l{'}apprentissage et l{'}{\'e}valuation des structures discontinues.
Incremental Discontinuous Phrase Structure Parsing with the GAP Transition
This article introduces a novel transition system for discontinuous lexicalized constituent parsing called SR-GAP.
Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks
We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2015; Plank et al., 2016).
Boosting for Efficient Model Selection for Syntactic Parsing
We present an efficient model selection method using boosting for transition-based constituency parsing.
Comparaison d'architectures neuronales pour l'analyse syntaxique en constituants
L{'}article traite de l{'}analyse syntaxique lexicalis{\'e}e pour les grammaires de constituants.
