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

LexFr: Adapting the LexIt Framework to Build a Corpus-Based French Subcategorization Lexicon Giulia Rambelli∗, Gianluca E. Lebani∗, Laurent Pr´evot†, Alessandro Lenci∗ ∗ Computational Linguistics Laboratory, University of Pisa, via Santa Maria, 36, Pisa (Italy) † Aix-Marseille University, avenue Pasteur, 5, Aix-en-Provence (France) g.rambelli1@studenti.unipi.it, gianluca.lebani@for.unipi.it, laurent.prevot@lpl-aix.fr, alessandro.lenci@unipi.it Abstract 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. Keywords: Automatic Lexical Acquisition, Subcategorization, Selectional Preferences, Evaluation of Lexical Resources 1. Introduction From the very beginning of the Natural Language Process- ing (NLP) enterprise, a crucial research topic has been the development of (semi-) automatic methods to build or en- rich lexical resources. It didn’t take long to realize that the accuracy advantage of the traditionally hand-built resources were counterbalanced by the higher cost-effectiveness and flexibility of the automatic methods, features that come in handy especially for languages, domains of topics for which hand-built resources are not available or are just too limited in their scope. Among the several kinds of information that can be in- cluded in a lexicon, the description of linguistic entities at the syntax-semantic interface proved to be useful for many traditional Natural Language Processing tasks such as word-sense disambiguation, machine translation, knowl- edge extraction (Schulte im Walde, 2009; Korhonen, 2009), so that the automatic acquisition of argument structure in- formation is a long-standing topic in computational linguis- tics. By embracing the theoretical assumption that the se- mantics of a predicate and the morpho-syntactic realization of its arguments are intimately related (Levin, 1993; Bres- nan, 1996; Roland and Jurafsky, 2002; Levin and Rappa- port Hovav, 2005), the last thirty years have witnessed the development of automatic methods for the identification of verb subcategorization frames (Korhonen et al., 2006; Mes- siant et al., 2008; Schulte im Walde, 2009), selectional pref- erences (Resnik, 1996; Light and Greiff, 2002; Erk et al., 2010) and diathesis alternation (McCarthy, 2001). The literature reports few examples of automatically built, wide coverage, lexica encoding this information, a.k.a. combinatorial lexica, among which notable mentions in- clude VALEX for English verbs (Korhonen et al., 2006), LexSchem from French verbs (Messiant et al., 2008) and LexIt for Italian verbs, nouns and adjectives (Lenci et al., 2012). These resources represent a reference point for the work presented in these pages, where we investigated the possibility to automatically extract distributional informa- tion about French predicates by …

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