Sense Disambiguation Using Online Dictionaries

Abstract

In this chapter, we introduce an approach to word sense disambiguation which involves mapping target words in context to relevant dictionary entries by taking advantage of linguistic structure and multiple sources of dictionary information. Linguistic structure, including argument and complement information, is extracted from the context of an occurrence of a target word and compared using semantic relations (synonyms and taxonyms). Use of this variety of cues—or multiple sources of information—requires a strategy for weighting various types of evidence. We apply information retrieval techniques to rank senses according to a similarity measure, because we have observed that selecting appropriate senses is similar to the task of retrieving documents, given a query. In both endeavors selections are based on comparing collections of cues to determine similarity. Without a “right” answer, a ranking with respect to relevance is appropriate. This ranking allows an application to determine which sense distinctions are required—rather than assuming fixed sense distinctions—when, in the course of an evolving “understanding” it is appropriate to make a commitment to sense distinctions.