@conference {13826,
	title = {Mapping lexical entries in a verbs database to WordNet senses},
	booktitle = {Proceedings of the 39th Annual Meeting on Association for Computational Linguistics},
	series = {ACL {\textquoteright}01},
	year = {2001},
	month = {2001///},
	pages = {244 - 251},
	publisher = {Association for Computational Linguistics},
	organization = {Association for Computational Linguistics},
	address = {Stroudsburg, PA, USA},
	abstract = {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.},
	doi = {10.3115/1073012.1073044},
	url = {http://dx.doi.org/10.3115/1073012.1073044},
	author = {Green,Rebecca and Pearl,Lisa and Dorr, Bonnie J and Resnik, Philip}
}
