Query Expansion with the Minimum User Feedback by Transductive Learning

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 963–970, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Query Expansion with the Minimum User Feedback by Transductive Learning Masayuki OKABE Information and Media Center Toyohashi University of Technology Aichi, 441-8580, Japan okabe@imc.tut.ac.jp Kyoji UMEMURA Information and Computer Sciences Toyohashi University of Technology Aichi, 441-8580, Japan umemura@tutics.tut.ac.jp Seiji YAMADA National Institute of Informatics Tokyo,101-8430, Japan seiji@nii.ac.jp Abstract Query expansion techniques generally se- lect 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 tech- nique 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 expan- sion technique. One is application of a transductive learning technique in order to increase relevant documents. The other is a modified parameter estimation method which laps the predictions by multiple learning trials and try to differentiate the importance of candidate terms for expan- sion in relevant documents. Experimen- tal results show that our technique outper- forms some traditional query expansion methods in several evaluation measures. 1 Introduction Query expansion is a simple but very useful tech- nique to improve search performance by adding some terms to an initial query. While many query expansion techniques have been proposed so far, a standard method of performing is to use relevance information from a user (Ruthven, 2003). If we can use more relevant documents in query expan- sion, the likelihood of selecting query terms achiev- ing high search improvement increases. However it is impractical to expect enough relevance informa- tion. Some researchers said that a user usually noti- fies few relevance feedback or nothing (Dumais and et al., 2003). In this paper we investigate the potential perfor- mance of query expansion under the condition that we can utilize little relevance information, espe- cially we only know a relevant document and a non- relevant document. To overcome the lack of rele- vance information, we tentatively increase the num- ber of relevant documents by a machine learning technique called Transductive Learning. Compared with ordinal inductive learning approach, this learn- ing technique works even if there is few training ex- amples. In our case, we can use many documents in a hit-list, however we know the relevancy of few documents. When applying query expansion, we use those increased documents as if they were true rel- evant ones. When applying the learning, there oc- curs some difficult problems of parameter settings. We also try to provide a reasonable resolution for the problems and show the effectiveness of our pro- posed method in experiments. The point of our query expansion method is that we focus on the availability of relevance information in practical situations. There are several researches …

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