Query expansion with the minimum relevance judgments

  • Authors:

  • Masayuki Okabe;Kyoji Umemura;Seiji Yamada

  • Affiliations:

  • Information and Media Center, Toyohashi University of Technology, Aichi, Japan;Information and Computer Science, Toyohashi University of Technology, Aichi, Japan;National Institute for Informatics, Tokyo, Japan

  • Venue:
  • AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
  • Year:
  • 2005

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Abstract

Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user’s manual judgment of those documents would much help to select good expansion terms, it is difficult to get enough feedback from users in practical situations. In this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document. In order to tackle this specific condition, we introduce two refinements to a well-known query expansion technique. One is to increase documents possibly being relevant by a transductive learning method because the more relevant documents will produce the better performance. The other is a modified term scoring scheme based on the results of the learning method and a simple function. Experimental results show that our technique outperforms some traditional methods in standard precision and recall criteria.