Mapping WorldNet Senses to a Lexical Database of Verbs

摘要

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.