NDLI: Selecting good expansion terms for pseudo-relevance feedback

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Selecting good expansion terms for pseudo-relevance feedback

Content Provider	ACM Digital Library
Author	Gao, Jianfeng Nie, Jian-Yun Robertson, Stephen Cao, Guihong
Abstract	Pseudo-relevance feedback assumes that most frequent terms in the pseudo-feedback documents are useful for the retrieval. In this study, we re-examine this assumption and show that it does not hold in reality - many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. We also show that good expansion terms cannot be distinguished from bad ones merely on their distributions in the feedback documents and in the whole collection. We then propose to integrate a term classification process to predict the usefulness of expansion terms. Multiple additional features can be integrated in this process. Our experiments on three TREC collections show that retrieval effectiveness can be much improved when term classification is used. In addition, we also demonstrate that good terms should be identified directly according to their possible impact on the retrieval effectiveness, i.e. using supervised learning, instead of unsupervised learning.
Starting Page	243
Ending Page	250
Page Count	8
File Format	PDF
ISBN	9781605581644
DOI	10.1145/1390334.1390377
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Expansion term classification Pseudo-relevance feedback Svm Language models
Content Type	Text
Resource Type	Article

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