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Optimisation of Character n-gram Profiles Method for Intrinsic Plagiarism Detection

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

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Abstract

The focus of the paper is to improve intrinsic plagiarism detection. The paper investigates and improves performance of character n-grams profiles method proposed by Stamatatos by tuning its parameter settings and proposing new modifications and rich feature sets. We raised the overall plagdet score from 24.67% to 33.41% for the PAN-PC09 corpus and from 18.83% to 26.66% for the PAN-PC11 corpus. Results are reported on PAN-PC09 and PAN-PC11 corpora, which are especially well suited for this task and were previously used in Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection (PAN) competitions.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Krakow, Poland

    Marcin Kuta & Jacek Kitowski

Authors
  1. Marcin Kuta
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  2. Jacek Kitowski
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

  3. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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Kuta, M., Kitowski, J. (2014). Optimisation of Character n-gram Profiles Method for Intrinsic Plagiarism Detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-07176-3_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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