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 Korhonen and Ryant (unpublished) have recently proposed another extension including 53 additional classes.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.
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Notes
See http://www.verbs.colorado.edu/verb-index/index.php for details.
See Kipper-Schuler (2005) for the full description of VN features.
See Korhonen and Briscoe (2004) for the details of this approach.
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Acknowledgments
This work was supported by National Science Foundation Grants NSF-9800658: VerbNet, NSF-9910603: ISLE, International Standards for Language Engineering, NSF-0415923: Advancing the Performance of Word Sense Disambiguation, the DTO-AQUAINT NBCHC040036 grant under the University of Illinois subcontract to the University of Pennsylvania 2003-07911-01, DARPA Grant N66001-00-1-8915 at the University of Pennsylvania, EPSRC project ‘ACLEX’ at the University of Cambridge Computer Laboratory (UK), and the Royal Society (UK).
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Appendix
Appendix
VerbNet description of the new verb class APPROVE
VerbNet description of the new verb class CONSUME
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Kipper, K., Korhonen, A., Ryant, N. et al. A large-scale classification of English verbs. Lang Resources & Evaluation 42, 21–40 (2008). https://doi.org/10.1007/s10579-007-9048-2
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DOI: https://doi.org/10.1007/s10579-007-9048-2