A relevance feedback mechanism for cluster-based retrieval

Authors:
Niall Rooney;David Patterson;Mykola Galushka;Vladimir Dobrynin
Affiliations:
Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Jordanstown, Newtownabbey, UK;Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Jordanstown, Newtownabbey, UK;Northern Ireland Knowledge Engineering Laboratory, University of Ulster, Jordanstown, Newtownabbey, UK;Faculty of Applied Mathematics & Control Processes, St. Petersburg State University, St. Petersburg, Russia
Venue:
Information Processing and Management: an International Journal
Year:
2006

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Abstract

Contextual document clustering is a novel approach which uses information theoretic measures to cluster semantically related documents bound together by an implicit set of concepts or themes of narrow specificity. It facilitates cluster-based retrieval by assessing the similarity between a query and the cluster themes' probability distribution. In this paper, we assess a relevance feedback mechanism, based on query refinement, that modifies the query's probability distribution using a small number of documents that have been judged relevant to the query. We demonstrate that by providing only one relevance judgment, a performance improvement of 33% was obtained.