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- W2207392393 abstract "The main concern of author identification is to define an appropriate characterization of documents that captures the writing style of authors. The most important approaches to computer-based author identification are exclusively based on lexical measures. In this paper we presented a fully automated approach to the identification of the authorship of unrestricted text by adapting a set of style markers to the analysis of the text. In this study, 35 style markers were applied to each author. By using our method, the author of a text can be identified by using the style markers that characterize a group of authors. The author group consists of 20 different writers. Author features including style markers were derived together with different machine learning algorithms. By using our method we have obtained a success rate of 80% in avarege." @default.
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- W2207392393 date "2007-08-01" @default.
- W2207392393 modified "2023-09-23" @default.
- W2207392393 title "Author Identification for Turkish Texts" @default.
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