On Improving Pseudo-Relevance Feedback using Pseudo-Irrelevant Documents

Pseudo-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 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 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.

In  ECIR 2010

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