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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Okabe, M., Umemura, K., Yamada, S. (2005). Query Expansion with the Minimum Relevance Judgments. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.H. (eds) Information Retrieval Technology. AIRS 2005. Lecture Notes in Computer Science, vol 3689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562382_3
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DOI: https://doi.org/10.1007/11562382_3
Publisher Name: Springer, Berlin, Heidelberg
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