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dc.contributor.authorRAMAN, K-
dc.contributor.authorUDUPA, R-
dc.contributor.authorBHATTACHARYA, P-
dc.contributor.authorBHOLE, A-
dc.identifier.citationADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS,5993,573-576en_US
dc.description.abstractPseudo-Relevance Feedback (PRF) assumes that the top-ranking 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 a 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 top-ranking 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.en_US
dc.source32nd Europeasn Conference on Information Retrieval Research,Milton Keynes, ENGLAND,MAR 28-31, 2010-
dc.subject.otherInformation Retrieval-
dc.subject.otherPseudo-Relevance Feedback-
dc.subject.otherQuery Expansion-
dc.subject.otherLinear Classifier-
dc.titleOn improving Pseudo-relevance feedback using Pseudo-irrelevant documentsen_US
dc.typeProceedings Paperen_US
Appears in Collections:Proceedings papers

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