Automatic Verb Classification Using Distributions of Grammatical Features

Proceedings of EACL '99 Automatic Verb Classification Using Distributions of Grammatical Features Suzanne Stevenson Dept of Computer Science and Center for Cognitive Science (RuCCS) Rutgers University CoRE Building, Busch Campus New Brunswick, NJ 08903 U.S.A. suzanne@ruccs, rutgers, edu Paola Merlo LATL-Department of Linguistics University of Geneva 2 rue de Candolle 1211 Gen~ve 4 SWITZERLAND merlo@lettres, unige, ch Abstract We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diathe- ses, extracted from a very large anno- tated corpus. Distributions of four gram- matical features are sufficient to reduce error rate by 50% over chance. We con- clude that corpus data is a usable repos- itory of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. 1 Introduction Recent years have witnessed a shift in grammar development methodology, from crafting large grammars, to annotation of corpora. Correspond- ingly, there has been a change from developing rule-based parsers to developing statistical meth- ods for inducing grammatical knowledge from an- notated corpus data. The shift has mostly oc- curred because building wide-coverage grammars is time-consuming, error prone, and difficult. The same can be said for crafting the rich lexical rep- resentations that are a central component of lin- guistic knowledge, and research in automatic lex- ical acquisition has sought to address this ((Dorr and Jones, 1996; Dorr, 1997), among others). Yet there have been few attempts to learn fine- grained lexical classifications from the statisti- cal analysis of distributional data, analogously to the induction of syntactic knowledge (though see, e.g., (Brent, 1993; Klavans and Chodorow, 1992; Resnik, 1992)). In this paper, we propose such an approach for the automatic classification of verbs into lexical semantic classes. 1 We can express the issues raised by this ap- proach as follows. 1. Which linguistic distinctions among lexical classes can we expect to find in a corpus? 2. How easily can we extract the frequency dis- tributions that approximate the relevant lin- guistic properties? 3. Which frequency distributions work best to distinguish the verb classes? In exploring these questions, we focus on verb classification for several reasons. Verbs are very important sources of knowledge in many language engineering tasks, and the relationships among verbs appear to play a major role in the orga- nization and use of this knowledge: Knowledge about verb classes is crucial for lexical acquisition in support of language generation and machine translation (Dorr, 1997), and document classifica- tion (Klavans and Kan, 1998). Manual classifica- tion of large numbers of verbs is a difficult and resource intensive task (Levin, 1993; Miller et ah, 1990; Dang et ah, 1998). To address these issues, we suggest that one can automatically classify verbs by using statistical approximations to verb diatheses, to train an au- tomatic classifier. We use verb diatheses, follow- ing Levin and Dorr, for two reasons. First, verb diatheses are syntactic cues to semantic classes, ~We are aware that a distributional approach rests on …

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