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 classified 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 disambiguated 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. (Cross-referenced as UMIACS-TR-2001-18) (Cross-referenced as LAMP-TR-068)