Improving Retrieval Performance with the Combination of Thesauri and Automatic Relevance Feedback

  • Mao-Zu Guo
  • Jian-Fu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


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.


Information Retrieval Relevance Feedback Related Word Query Expansion Information Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mao-Zu Guo
    • 1
  • Jian-Fu Li
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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