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Authorship Attribution for Polish Texts Based on Part of Speech Tagging

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Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation (BDAS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 716))

Abstract

Authorship attribution aims at identifying the author of an unseen text document based on text samples originating from different authors. In this paper we focus on authorship attribution of Polish texts using stylometric features based on part of speech (POS) tags. Polish language is characterized by high inflection level and in consequence over 1000 POS tags can be distinguished. This allows building a sufficiently large feature space by extracting POS information from documents and performing their classification with use of machine learning methods. We report results of experiments conducted with Weka workbench using combinations of the following features: POS tags, an approximation of their bigrams and simple document statistics.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Function_(mathematics).

  2. 2.

    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. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Szwed, P. (2017). Authorship Attribution for Polish Texts Based on Part of Speech Tagging. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-58274-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58273-3

  • Online ISBN: 978-3-319-58274-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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