Abstract
This paper examines the machine learning approach to authorship attribution of articles in the Polish language. The focus is on the effect of the data volume, number of authors and thematic homogeneity on authorship attribution quality. We study the impact of feature selection under various feature selection criteria, mainly chi square and information gain measures, as well as the effect of combining features of different types. Results are reported for the Rzeczpospolita corpus in terms of the \(F_1\) measure.
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Acknowledgments
This research is supported by AGH - University of Science and Technology (AGH-UST) Grant no. 11.11.230.124 (statutory project).
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Kuta, M., Puto, B., Kitowski, J. (2016). Authorship Attribution of Polish Newspaper Articles. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_41
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DOI: https://doi.org/10.1007/978-3-319-39384-1_41
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