Paper: Mapping Lexical Entries In A Verbs Database To WordNet Senses

ACL ID P01-1032
Title Mapping Lexical Entries In A Verbs Database To WordNet Senses
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

This paper describes automatic tech- niques for mapping 9611 entries in a database of English verbs to Word- Net senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 disambiguated entries, representing 1442 verb entries from 167 classes; (2) word sense probabilities, from frequency counts in a tagged corpus; (3) semantic similarity of WordNet senses for verbs within the same class; (4) probabilistic correlations between WordNet data and attributes of the verb classes. The best results achieved 72% precision and 58% rec...