Abstract
A new effective system for identification and verification of text authorship has been developed. The system is created on the basis of machine learning. The originality of the model is caused by a suggested unique profile of the author’s style features. Together with the use of the Support Vector Machine method, this allows us to achieve the high accuracy of the authorship detection. Proposed method allows the system to learn styles for a large number of authors using small amount of data in a training set.
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Acknowledgments
The authors of the article are grateful to Phase One: Karma LTD company, especially to the Unplag team for the support in research and considerable assistance in the development, testing and implementation of the authorship attribution method.
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Marchenko, O., Anisimov, A., Nykonenko, A., Rossada, T., Melnikov, E. (2017). Authorship Attribution System. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_27
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DOI: https://doi.org/10.1007/978-3-319-59569-6_27
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