Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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Although Pseudo-Relevance Feedback (PRF) techniques improve average retrieval performance at the price of high variance, not much is known about their optimality and the reasons for their instability. In this work, we study more than 800 topics from several test collections including the TREC Robust Track and show that PRF techniques are highly suboptimal, i.e. they do not make the fullest utilization of pseudo-relevant documents and under-perform. A careful selection of expansion terms from the pseudo-relevant document with the help of an oracle can actually improve retrieval performance dramatically (by 60%). Further, we show that instability in PRF techniques is mainly due to wrong selection of expansion terms from the pseudo-relevant documents. Our findings emphasize the need to revisit the problem of term selection to make a break through in PRF.