Query Expansion with the Minimum Relevance Judgments

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.

• Type: Conference
• Year: 2005
• Where: AIRS
• Authors: Masayuki Okabe, Kyoji Umemura, Seiji Yamada
• Keywords: AIRS 2005, Information Retrieval, Query Expansion Technique, Relevant Documents, Well-known Query Expansion