Mapping WorldNet Senses to a Lexical Database of Verbs
| Title | Mapping WorldNet Senses to a Lexical Database of Verbs |
| Publication Type | Reports |
| Year of Publication | 2001 |
| Authors | Green R, Pearl L, Dorr BJ |
| Date Published | 2001/01// |
| Institution | Instititue for Advanced Computer Studies, Univ of Maryland, College Park |
| 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) |
| 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. |
| URL | http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458846 |