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2001

Mapping Lexical Entries in a Verbs Database to WordNet Senses

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Mapping Lexical Entries in a Verbs Database to WordNet Senses
This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to WordNet 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% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring WordNet sense, and an upper bound of 87% precision and 75% recall for human judgment.
Rebecca Green, Lisa Pearl, Bonnie J. Dorr, Philip
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where ACL
Authors Rebecca Green, Lisa Pearl, Bonnie J. Dorr, Philip Resnik
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