Word Sense Disambiguation

Abstract: 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 set of related senses.

Word Sense Ambiguation: Clustering Related Senses
Paper from Microsoft Research (MS Word Document)

"This paper describes a heuristic approach to automatically identifying which senses of a machine- readable 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 set of 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."

Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance (PDF)

I Don't Believe in Word Senses (1997 paper: PDF)

Abstract: Word sense disambiguation assumes word senses. Within the lexicography and linguistics literature, they are known to be very slippery entities. The paper looks at problems with existing accounts of `word sense' and describes the various kinds of ways in which a word's meaning can deviate from its core meaning. An analysis is presented in which word senses are abstractions from clusters of corpus citations, in accordance with current lexicographic practice.