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Stylometric Features for Authorship Attribution of Polish Texts

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

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Abstract

Authorship attribution aims at distinguishing texts written by different authors using text features representing their styles. In this paper we investigate stylometric features for the Polish language based on Part of Speech (POS) tagging (including POS bigrams) and function words. Due to high inflection level of Polish language the feature space tends to be very large. This in particular concerns POS n-grams. Focusing on POS bigrams, we propose their simplified representation allowing to keep the feature space compact. We report experiments, in which authorship attribution was conducted for varying in lengths documents, with use of classifiers from the Weka library. We evaluate classification results for combinations of the following features: POS tags, POS bigrams, function words and simple document statistics. Experiments indicate that the developed features provide good classification performance.

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Notes

  1. 1.

    https://sites.google.com/site/computationalstylistics/.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Kraków, Poland

    Piotr Szwed

Authors
  1. Piotr Szwed
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Correspondence to Piotr Szwed .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Szwed, P. (2017). Stylometric Features for Authorship Attribution of Polish Texts. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-59060-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

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