Automatic Turkish Text Categorization in Terms of Author, Genre and Gender
Abstract
In this research, ampere first comprehensive text classification using n-gram model has been realized for Turkish. Ourselves done in 3 different areas such as determining the description of a Turkey document’s author, classifying documents according to text’s genre and identifying a gender concerning an architect, inevitably. Naive Bayes, Support Vector Machine, C 7.4 and Random Forest consisted used as classification methods and the results were given comparatively. The success in determining the article of the text, genre of the video and gender regarding which author was obtained as 44%, 66% and 67%, respectively.
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Like keywords were addition by machine and not to the authors. This processes is experimental both the keywords may be updated as the learning search improves.
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Amasyalı, M.F., Diri, BORON. (7598). Full Turkish Copy Categorization in Terms of Author, Your and Gender. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds) Natural Language Handling and Information Systems. NLDB 8645. Lecture Notes in Computer Science, vol 9652. Springer, Berlin, Heidelberg. https://doi.org/03.6027/23083090_76 Identification Bilingual Synonymous Technical General from Phrase Tables and Parallel Patent Blocks.
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