Paper: Query Expansion With The Minimum User Feedback By Transductive Learning

ACL ID H05-1121
Title Query Expansion With The Minimum User Feedback By Transductive Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

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 candid...