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2010
Springer

On Improving Pseudo-Relevance Feedback Using Pseudo-Irrelevant Documents

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On Improving Pseudo-Relevance Feedback Using Pseudo-Irrelevant Documents
Abstract. Pseudo-Relevance Feedback (PRF) assumes that the topranking n documents of the initial retrieval are relevant and extracts expansion terms from them. In this work, we introduce the notion of pseudo-irrelevant documents, i.e. high-scoring documents outside of top n that are highly unlikely to be relevant. We show how pseudo-irrelevant documents can be used to extract better expansion terms from the topranking n documents: good expansion terms are those which discriminate the top-ranking n documents from the pseudo-irrelevant documents. Our approach gives substantial improvements in retrieval performance over Model-based Feedback on several test collections. Key words: Information Retrieval, Pseudo-Relevance Feedback, Query Expansion, Pseudo-Irrelevance, Linear Classifier
Karthik Raman, Raghavendra Udupa, Pushpak Bhattach
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where ECIR
Authors Karthik Raman, Raghavendra Udupa, Pushpak Bhattacharyya, Abhijit Bhole
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