Abstract

This research presents the development of a more robust model for measuring semantic similarity than those currently available for solving the problem of word sense disambiguation applied to natural language processing. The model is based both linguistically and statistically on the interaction of two approaches to taxonomic exploration: path-based and information content, through the incorporation of FunGramKB as a sense inventory. In terms of evaluation, the proposed similarity measure consistently generated efficient results from a linguistic perspective in the automatic lexical disambiguation process.