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dc.contributor.authorErcan, G.en_US
dc.contributor.authorCicekli, I.en_US
dc.date.accessioned2016-02-08T09:44:44Z
dc.date.available2016-02-08T09:44:44Z
dc.date.issued2012en_US
dc.identifier.issn0165-5515
dc.identifier.urihttp://hdl.handle.net/11693/21321
dc.description.abstractPrevious research shows that keyphrases are useful tools in document retrieval and navigation. While these point to a relation between keyphrases and document retrieval performance, no other work uses this relationship to identify keyphrases of a given document. This work aims to establish a link between the problems of query performance prediction (QPP) and keyphrase extraction. To this end, features used in QPP are evaluated in keyphrase extraction using a naïve Bayes classifier. Our experiments indicate that these features improve the effectiveness of keyphrase extraction in documents of different length. More importantly, commonly used features of frequency and first position in text perform poorly on shorter documents, whereas QPP features are more robust and achieve better results. © 2012 The Author(s).en_US
dc.language.isoEnglishen_US
dc.source.titleJournal of Information Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1177/0165551512448984en_US
dc.subjectKeyphrase extractionen_US
dc.subjectQuery performance predictionen_US
dc.subjectBayes classifieren_US
dc.subjectDocument retrievalen_US
dc.subjectKeyphrase extractionen_US
dc.subjectQuery performance predictionen_US
dc.subjectInformation scienceen_US
dc.subjectInformation systemsen_US
dc.subjectInformation retrievalen_US
dc.titleKeyphrase extraction through query performance predictionen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage476en_US
dc.citation.epage488en_US
dc.citation.volumeNumber38en_US
dc.citation.issueNumber5en_US
dc.identifier.doi10.1177/0165551512448984en_US
dc.publisherSage Publications Ltd.en_US


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