Abstract
The ever growing popularity of the Internet as a source of information, coupled with the accompanying growth in the number of documents available through the World Wide Web, is leading to an increasing demand for more efficient and accurate information retrieval tools. One of the fundamental problems in information retrieval is word mismatch. Expanding a user’s query with related words can improve the search performance, but the finding and using of related words is still an open problem. On the basis of previous approaches to query expansion, this paper proposes a new approach to query expansion that combines two popular traditional methods—thesauri and automatic relevance feedback. According to theoretical analysis and experiments, the new approach can effectively improve the web retrieval performance and out-performs the optimized conventional expansion approaches.
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© 2006 Springer-Verlag Berlin Heidelberg
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Guo, MZ., Li, JF. (2006). Improving Retrieval Performance with the Combination of Thesauri and Automatic Relevance Feedback. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_34
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DOI: https://doi.org/10.1007/11739685_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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