%0 Conference Paper %B Proceedings of the 39th Annual Meeting on Association for Computational Linguistics %D 2001 %T Mapping lexical entries in a verbs database to WordNet senses %A Green,Rebecca %A Pearl,Lisa %A Dorr, Bonnie J %A Resnik, Philip %X 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. %B Proceedings of the 39th Annual Meeting on Association for Computational Linguistics %S ACL '01 %I Association for Computational Linguistics %C Stroudsburg, PA, USA %P 244 - 251 %8 2001/// %G eng %U http://dx.doi.org/10.3115/1073012.1073044 %R 10.3115/1073012.1073044