A comparative study of author gender identification

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dc.WoS.categoriesComputer Science, Artificial Intelligence; Engineering, Electrical & Electronicen_US
dc.contributor.authorYıldız, Tuğba
dc.date.accessioned2021-02-24T06:56:12Z
dc.date.available2021-02-24T06:56:12Z
dc.date.issued2019
dc.description.abstractIn recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their associated performances are assessed. A variety of experiments are successfully conducted. Different issues, such as the effects of the number of dimensions, training architecture type, and corpus size, are considered. The main contribution of the study is to identify author gender by applying word embeddings and deep learning architectures to the Turkish language.en_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.3906/elk-1806-185
dc.identifier.issn1303-6203
dc.identifier.issn1300-0632
dc.identifier.scopus2-s2.0-85065830112en_US
dc.identifier.trdizinid336592en_US
dc.identifier.urihttps://hdl.handle.net/11411/3277
dc.identifier.urihttps://doi.org/10.3906/elk-1806-185
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/336592en_US
dc.identifier.wosWOS:000463355800028en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.issue2en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors1en_US
dc.pages1052-1064en_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAuthor gender identificationen_US
dc.subjectconvolution neural networken_US
dc.subjectrecurrent neural networken_US
dc.subjectWord2vecen_US
dc.subjectDoc2vecen_US
dc.titleA comparative study of author gender identification
dc.typeArticle
dc.volume27en_US

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