A new approach to search result clustering and labeling

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dc.citation.epage292en_US
dc.citation.spage283en_US
dc.citation.volumeNumber7097en_US
dc.contributor.authorTürel, Anılen_US
dc.contributor.authorCan, Fazlıen_US
dc.coverage.spatialDubai, United Arab Emiratesen_US
dc.date.accessioned2016-02-08T12:15:17Z
dc.date.available2016-02-08T12:15:17Z
dc.date.issued2011en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: 7th Asia Information Retrieval Societies Conference, AIRS 2011en_US
dc.descriptionDate of Conference: December 18-20, 2011en_US
dc.description.abstractSearch engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:15:17Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2011en
dc.identifier.doi10.1007/978-3-642-25631-8_26en_US
dc.identifier.doi10.1007/978-3-642-25631-8en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/28246
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-25631-8_26en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-642-25631-8en_US
dc.source.titleInformation Retrieval Technologyen_US
dc.subjectCluster labelingen_US
dc.subjectSearch result clusteringen_US
dc.subjectWeb information retrievalen_US
dc.subjectCluster contenten_US
dc.subjectCluster labelingen_US
dc.subjectClustering approachen_US
dc.subjectClustering methodsen_US
dc.subjectClustering qualityen_US
dc.subjectData setsen_US
dc.subjectK-Means clustering algorithmen_US
dc.subjectPost processingen_US
dc.subjectPrecision and recallen_US
dc.subjectQuery resultsen_US
dc.subjectRelative performanceen_US
dc.subjectResearch problemsen_US
dc.subjectSearch resultsen_US
dc.subjectSuffix-treesen_US
dc.subjectTerm weightingen_US
dc.subjectWeb information retrievalen_US
dc.subjectClustering algorithmsen_US
dc.subjectContent based retrievalen_US
dc.subjectInformation retrievalen_US
dc.subjectInfrared devicesen_US
dc.subjectWorld Wide Weben_US
dc.subjectSearch enginesen_US
dc.titleA new approach to search result clustering and labelingen_US
dc.typeConference Paperen_US

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