"A term is known by the company it keeps": On Selecting a Good Expansion Set in Pseudo-Relevance Feedback

Abstract. It is well known that pseudo-relevance feedback (PRF) improves the retrieval performance of Information Retrieval (IR) systems in general. However, a recent study by Cao et al [3] has shown that a non-negligible fraction of expansion terms used by PRF algorithms are harmful to the retrieval. In other words, a PRF algorithm would be better off if it were to use only a subset of the feedback terms. The challenge then is to find a good expansion set from the set of all candidate expansion terms. A natural approach to solve the problem is to make term independence assumption and use one or more term selection criteria or a statistical classifier to identify good expansion terms independent of each other. In this work, we challenge this approach and show empirically that a feedback term is neither good nor bad in itself in general; the behavior of a term depends very much on other expansion terms. Our finding implies that a good expansion set can not be found by making term in...

• Type: Conference
• Year: 2009
• Where: ICTIR
• Authors: Raghavendra Udupa, Abhijit Bhole, Pushpak Bhattacharyya
• Keywords: Expansion Terms, Feedback Term, ICTIR 2009, Information Retrieval, Term Independence Assumption