Abstract
The aim of this study is to identify the author of an unauthorized document. Ten different feature vectors are obtained from authorship attributes, n-grams and various combinations of these feature vectors that are extracted from documents, which the authors are intended to be identified. Comparative performance of every feature vector is analyzed by applying Naïve Bayes, SVM, k-NN, RF and MLP classification methods. The most successful classifiers are MLP and SVM. In document classification process, it is observed that n-grams give higher accuracy rates than authorship attributes. Nevertheless, using n-gram and authorship attributes together, gives better results than when each is used alone.
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Türkoğlu, F., Diri, B., Amasyalı, M.F. (2007). Author Attribution of Turkish Texts by Feature Mining. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_110
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DOI: https://doi.org/10.1007/978-3-540-74171-8_110
Publisher Name: Springer, Berlin, Heidelberg
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