@article {13827,
	title = {Mapping WorldNet Senses to a Lexical Database of Verbs},
	year = {2001},
	month = {2001/01//},
	institution = {Instititue for Advanced Computer Studies, Univ of Maryland, College Park},
	abstract = {This paper describes automatic techniques for mapping 9611 semantically classified English verbs to WordNet senses. The verbs were initially grouped into 491 semantic classes based on syntactic categories; they were then mapped into WordNet senses according to three pieces of information: (1) prior probability of WordNet senses; (2) semantic similarity of WordNet senses for verbs within the same category; and (3) probabilistic correlations between WordNet relationship and verb frame data. Our techniques make use of a training set of 1791 disambiguated entries representing 1442 verbs occurring in 167 of the categories. The best results achieved .58 recall and .72 precision, versus a lower bound of .38 recall and .62 precision for assigning the most frequently occurring WordNet sense, and an upper bound of .75 recall and .87 precision for human judgment.},
	keywords = {*DATA BASES, *ENGLISH LANGUAGE, *LEXICAL DATABASES, *LEXICOGRAPHY, *MAPPING, *VERBS, *WORD MAPPING, *WORDNET, AMBIGUITY, correlation, FRAMES, Frequency, INFORMATION SCIENCE, JUDGEMENT(PSYCHOLOGY), linguistics, PRECISION, probability, RECALL, semantics, syntax, WORDS(LANGUAGE), WSD(WORD SENSE DISAMBIGUATION)},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA458846},
	author = {Green,Rebecca and Pearl,Lisa and Dorr, Bonnie J}
}
