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dc.contributor.authorAbbasi, Muhammad Kamranen
dc.contributor.authorFrommholz, Ingoen
dc.date.accessioned2014-10-24T11:13:09Z
dc.date.available2014-10-24T11:13:09Z
dc.date.issued2014-04
dc.identifier.citationAbbasi, M.K., Frommholz, I. (2014) 'Exploiting information needs and bibliographics for polyrepresentative document clustering', In Proceedings of the Bibliometrics-enhanced Information Retrieval Workshop at ECIR 2014, Amsterdam: 21–28en
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/10547/333127
dc.description.abstractIn this paper we explore the potential of combining the principle of polyrepresentation with document clustering. Our idea is discussed and evaluated for polyrepresentation of information needs as wells as for document-based polyrepresentation where bibliographic information is used as representation. The main idea is to present the user with the highly ranked polyrepresentative clusters to support the search process. Our evaluation suggests that our approach is capable of increasing retrieval performance, but performance varies for queries with a high or low number of relevant documents.
dc.language.isoenen
dc.publisherCEUR-WSen
dc.relation.urlhttp://ceur-ws.org/Vol-1143/en
dc.subjectpolyrepresentationen
dc.subjectdocument clusteringen
dc.subjectbibliographicsen
dc.subjectinformation needsen
dc.subjectinformation retrievalen
dc.titleExploiting information needs and bibliographics for polyrepresentative document clusteringen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalProceedings of the Bibliometrics-enhanced Information Retrieval Workshop at ECIR 2014en
html.description.abstractIn this paper we explore the potential of combining the principle of polyrepresentation with document clustering. Our idea is discussed and evaluated for polyrepresentation of information needs as wells as for document-based polyrepresentation where bibliographic information is used as representation. The main idea is to present the user with the highly ranked polyrepresentative clusters to support the search process. Our evaluation suggests that our approach is capable of increasing retrieval performance, but performance varies for queries with a high or low number of relevant documents.


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