A supervised algorithm for verb disambiguation into VerbNet classes

Omri Abend*, Roi Reichart, Ari Rappoport

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

VerbNet (VN) is a major large-scale English verb lexicon. Mapping verb instances to their VN classes has been proven useful for several NLP tasks. However, verbs are polysemous with respect to their VN classes. We introduce a novel supervised learning model for mapping verb instances to VN classes, using rich syntactic features and class membership constraints. We evaluate the algorithm in both in-domain and corpus adaptation scenarios. In both cases, we use the manually tagged Sem-link WSJ corpus as training data. For in-domain (testing on Semlink WSJ data), we achieve 95.9% accuracy, 35.1% error reduction (ER) over a strong baseline. For adaptation, we test on the GENIA corpus and achieve 72.4% accuracy with 10.7% ER. This is the first large-scale experimentation with automatic algorithms for this task.

Original languageAmerican English
Title of host publicationColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages9-16
Number of pages8
ISBN (Print)9781905593446
DOIs
StatePublished - 2008
Externally publishedYes
Event22nd International Conference on Computational Linguistics, Coling 2008 - Manchester, United Kingdom
Duration: 18 Aug 200822 Aug 2008

Publication series

NameColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
Volume1

Conference

Conference22nd International Conference on Computational Linguistics, Coling 2008
Country/TerritoryUnited Kingdom
CityManchester
Period18/08/0822/08/08

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