Jon Curtis, John Cabral, David Baxter
This paper describes a novel, unsupervised method of word sense disambiguation that is wholly semantic, drawing upon a complex, rich ontology and inference engine (the Cyc system). This method goes beyond more familiar semantic closeness approaches to disambiguation that rely on string co-occurrence or relative location in a taxonomy or concept map by 1) exploiting a rich array of properties, including higher-order properties, not available in merely taxonomic (or other first-order) systems, and 2) appealing to the semantic contribution a word sense makes to the content of the target text. Experiments show that this method produces results markedly better than chance when disambiguating word senses in a corpus of topically unrelated documents.
Subjects: 13. Natural Language Processing; 11.2 Ontologies
Submitted: Feb 13, 2006