A large-scale classification of English verbs

Abstract

Lexical classifications have proved useful in supporting various natural language processing (NLP) tasks. The largest verb classification for English is Levin’s (1993) work which defines groupings of verbs based on syntactic and semantic properties. VerbNet (VN) (Kipper et al. 2000; Kipper-Schuler 2005)—an extensive computational verb lexicon for English—provides detailed syntactic-semantic descriptions of Levin classes. While the classes included are extensive enough for some NLP use, they are not comprehensive. Korhonen and Briscoe (2004) have proposed a significant extension of Levin’s classification which incorporates 57 novel classes for verbs not covered (comprehensively) by Levin. Korhonen and Ryant (unpublished) have recently proposed another extension including 53 additional classes. This article describes the integration of these two extensions into VN. The result is a comprehensive Levin-style classification for English verbs providing over 90% token coverage of the Proposition Bank data (Palmer et al. 2005) and thus can be highly useful for practical applications.