Word Sense Ambiguation: Clustering Related Senses

This paper describes a heuristic approach to automatically identifying which senses of a machinereadable dictionary (MRD) headword are semantically related versus those which correspond to fundamentally different senses of the word. The inclusion of this information in a lexical database profoundly alters the nature of sense disambiguation: the appropriate "sense" of a polysemous word may now correspond to some s e t o f related senses. Our technique offers benefits both for on-line semantic processing and for the challenging task of mapping word senses across multiple MRDs in creating a merged lexical database.