トランスダクティブ学習による最小文書判定からのクエリ拡張

抄録

Query expansion is a technique of information retrieval to select new query terms which improve search performance. Although good terms can be extracted from documents whose relevancy has already been known, it is difficult to get enough such feedback from users in practical situations. In this paper we propose a query expansion method which performs well even if a user only notifies relevancy of documents until just a relevant one is found. In order to tackle this specific condition, we introduce two refinements to a well-known query expansion method. One is the application of transductive learning to increase the amount of latent relevant documents. The other is the introduction of a modified parameter estimation method which laps the predictions of multiple learning trials in order to differentiate the importance of candidate terms for expansion. Experimental results show that our method outperforms traditional methods when an initial search fails.