Abstract
In this study, a first comprehensive text classification using n-gram model has been realized for Turkish. We worked in 3 different areas such as determining the identification of a Turkish document’s author, classifying documents according to text’s genre and identifying a gender of an author, automatically. Naive Bayes, Support Vector Machine, C 4.5 and Random Forest were used as classification methods and the results were given comparatively. The success in determining the author of the text, genre of the text and gender of the author was obtained as 83%, 93% and 96%, respectively.
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© 2006 Springer-Verlag Berlin Heidelberg
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Amasyalı, M.F., Diri, B. (2006). Automatic Turkish Text Categorization in Terms of Author, Genre and Gender. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2006. Lecture Notes in Computer Science, vol 3999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11765448_22
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DOI: https://doi.org/10.1007/11765448_22
Publisher Name: Springer, Berlin, Heidelberg
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