Presentation 2004/11/30
Query Expansion with the Minimum Judgement(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
MASAYUKI OKABE, SEIJI YAMADA,
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Abstract(in English) Query expansion is one of feedback techniques in information retrieval, which needs a certain amount of relevance information that costs high in terms of human effort. In this paper we propose a method of query expansion which utilizes human help but with the minimum cost. Our purpose is to reduce users' cost when judging the relevancy of documents as much as possible using Transductive Learning. We describe this learning method is used to predict the relevancy of documents with no manual judgement based on only a fraction of true relevance information. We also show the role of the learning in our query expansion procedure. Com-pared with traditional query expansion methods, our method show the distinct effectiveness of query expansion, especially in the top 10 or 20 documents.
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Paper # AI2004-52
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Committee AI
Conference Date 2004/11/30(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Query Expansion with the Minimum Judgement(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
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1st Author's Name MASAYUKI OKABE
1st Author's Affiliation Toyohashi University of Technology()
2nd Author's Name SEIJI YAMADA
2nd Author's Affiliation National Institute of Informatics
Date 2004/11/30
Paper # AI2004-52
Volume (vol) vol.104
Number (no) 488
Page pp.pp.-
#Pages 6
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