Mapping Lexical Entries in

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 categories. Mapping these classiied verbs into WordNet senses provides a resource that may be used for disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 dis-ambiguated entries, representing 1442 verb entries from 167 of the categories; (2) word sense probabilities based on frequency counts in a previously 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 .