LexFr: Adapting the LexIt Framework to Build a Corpus-Based French Subcategorization Lexicon

Giulia Rambelli (1) , Gianluca E Lebani , Laurent Prevot (2) , Alessandro Lenci

1 University of Pisa - Università di Pisa
2 AMU - Aix Marseille Université

This paper introduces LexFr, a corpus-based French lexical resource built by adapting the framework LexIt, originally developed to describe the combinatorial potential of Italian predicates. As in the original framework, the behavior of a group of target predicates is characterized by a series of syntactic (i.e., subcategorization frames) and semantic (i.e., selectional preferences) statistical information (a.k.a. distributional profiles) whose extraction process is mostly unsupervised. The first release of LexFr includes information for 2,493 verbs, 7,939 nouns and 2,628 adjectives. In these pages we describe the adaptation process and evaluated the final resource by comparing the information collected for 20 test verbs against the information available in a gold standard dictionary. In the best performing setting, we obtained 0.74 precision, 0.66 recall and 0.70 F-measure.

Mots clés: Evaluation of Lexical Resources; Selectional Preferences; Subcategorization; Automatic Lexical Acquisition; Evaluation of Lexical Resources

Domaines: Informatique et langage [cs.CL]

Giulia Rambelli, Gianluca E Lebani, Laurent Prevot, Alessandro Lenci. LexFr: Adapting the LexIt Framework to Build a Corpus-Based French Subcategorization Lexicon. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), May 2016, Portorož, Slovenia. pp.930-937. ⟨hal-03312844⟩