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dc.contributor.authorSüzek, Tuğba Önal
dc.date.accessioned2020-11-20T14:54:43Z
dc.date.available2020-11-20T14:54:43Z
dc.date.issued2017
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.urihttps://doi.org/10.3906/elk-1511-203
dc.identifier.urihttps://hdl.handle.net/20.500.12809/2169
dc.descriptionWOS: 000404385700015en_US
dc.description.abstractIn this study, we describe a keyword extraction technique that uses latent semantic analysis (LSA) to identify semantically important single topic words or keywords. We compare our method against two other automated keyword extractors, Tf-idf (term frequency-inverse document frequency) and Metamap, using human-annotated keywords as a reference. Our results suggest that the LSA-based keyword extraction method performs comparably to the other techniques. Therefore, in an incremental update setting, the LSA-based keyword extraction method can be preferably used to extract keywords from text descriptions from big data when compared to existing keyword extraction methods.en_US
dc.description.sponsorshipTUBITAK 2232 Reintegration FellowshipTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [113C030]en_US
dc.description.sponsorshipThis work was supported in part by the TUBITAK 2232 Reintegration Fellowship Grant 113C030.en_US
dc.item-language.isoengen_US
dc.publisherTubitak Scientific & Technical Research Council Turkeyen_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBioinformaticsen_US
dc.subjectText Miningen_US
dc.subjectInformation Retrievalen_US
dc.titleUsing latent semantic analysis for automated keyword extraction from large document corporaen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.institutionauthorSüzek, Tuğba Önal
dc.identifier.doi10.3906/elk-1511-203
dc.identifier.volume25en_US
dc.identifier.issue3en_US
dc.identifier.startpage1784en_US
dc.identifier.endpage1794en_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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