An Information Retrieval Approach to Sense Ranking

In word sense disambiguation, choosing the most frequent sense for an ambiguous word is a powerful heuristic. However, its usefulness is restricted by the availability of sense-annotated data. In this paper, we propose an information retrieval-based method for sense ranking that does not require annotated data. The method queries an information retrieval engine to estimate the degree of association between a word and its sense descriptions. Experiments on the Senseval test materials yield state-ofthe-art performance. We also show that the estimated sense frequencies correlate reliably with native speakers’ intuitions.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Mirella Lapata, Frank Keller
• Keywords: Computational Linguistics, Frequent Sense, NAACL 2007, Sense Ranking, Word Sense Disambiguation