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 ABSTRACT
The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insufficiencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need different strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the difference of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.
REFERENCES
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CITED BY 36
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Dou Shen , Rong Pan , Jian-Tao Sun , Jeffrey Junfeng Pan , Kangheng Wu , Jie Yin , Qiang Yang, Q2C@UST: our winning solution to query classification in KDDCUP 2005, ACM SIGKDD Explorations Newsletter, v.7 n.2, p.100-110, December 2005
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Dou Shen , Rong Pan , Jian-Tao Sun , Jeffrey Junfeng Pan , Kangheng Wu , Jie Yin , Qiang Yang, Query enrichment for web-query classification, ACM Transactions on Information Systems (TOIS), v.24 n.3, p.320-352, July 2006
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Bin Cui , Ling Liu , Calton Pu , Jialie Shen , Kian-Lee Tan, QueST: querying music databases by acoustic and textual features, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany
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