Authorship Attribution System
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.
KeywordsMachine learning Support Vector Machine Authorship detection
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.
- 1.Lewis, D., Yang, Y., Rose, T., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)Google Scholar
- 2.Escalante, H., Solorio, T., Montes-y-Gomez, M.: Local histograms of character N-grams for authorship attribution. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, June 2011Google Scholar
- 4.Ramnial, H., Panchoo, S., Pudaruth, S.: Authorship attribution using stylometry and machine learning techniques. In: Berretti, S., Thampi, S.M., Srivastava, P.R. (eds.) Intelligent Systems Technologies and Applications. AISC, vol. 384, pp. 113–125. Springer, Cham (2016). doi: 10.1007/978-3-319-23036-8_10 CrossRefGoogle Scholar
- 5.Ruder, S., Ghaffari, P., Breslin, J.: Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution. CoRR (2016)Google Scholar