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ABSTRACT
Word sense ambiguity is recognized as having a detrimental effect on the precision of information retrieval systems in general and web search systems in particular, due to the sparse nature of the queries involved. Despite continued research into the application of automated word sense disambiguation, the question remains as to whether less than 90% accurate automated word sense disambiguation can lead to improvements in retrieval effectiveness. In this study we explore the development and subsequent evaluation of a statistical word sense disambiguation system which demonstrates increased precision from a sense based vector space retrieval model over traditional TF*IDF techniques.
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Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 14
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David Vogel , Steffen Bickel , Peter Haider , Rolf Schimpfky , Peter Siemen , Steve Bridges , Tobias Scheffer, Classifying search engine queries using the web as background knowledge, ACM SIGKDD Explorations Newsletter, v.7 n.2, p.117-122, December 2005
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