Sense disambiguation using semantic relations and adjacency information

Abstract

This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. In all, the disambiguator uses 39 heuristics based on 12 relationships.