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dc.contributor.authorKucukyilmaz, T.en_US
dc.contributor.authorCambazoglu, B. B.en_US
dc.contributor.authorAykanat, C.en_US
dc.contributor.authorCan, F.en_US
dc.date.accessioned2016-02-08T11:49:03Z
dc.date.available2016-02-08T11:49:03Z
dc.date.issued2006en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27262
dc.description.abstractThe aim of this paper is to investigate the feasibility of predicting the gender of a text document's author using linguistic evidence. For this purpose, term- and style-based classification techniques are evaluated over a large collection of chat messages. Prediction accuracies up to 84.2% are achieved, illustrating the applicability of these techniques to gender prediction. Moreover, the reverse problem is exploited, and the effect of gender on the writing style is discussed. © Springer-Verlag Berlin Heidelberg 2006.en_US
dc.language.isoEnglishen_US
dc.source.titleLecture Notes in Computer Scienceen_US
dc.relation.isversionofhttps://doi.org/10.1007/11890393_29en_US
dc.subjectClassification (of information)en_US
dc.subjectInformation retrievalen_US
dc.subjectLinguisticsen_US
dc.subjectProblem solvingen_US
dc.subjectText processingen_US
dc.subjectChat messagesen_US
dc.subjectChat miningen_US
dc.subjectGender predictionen_US
dc.subjectInformation analysisen_US
dc.titleChat mining for gender predictionen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage274en_US
dc.citation.epage283en_US
dc.citation.volumeNumber4243en_US
dc.identifier.doi10.1007/11890393_29en_US
dc.publisherSpringeren_US


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