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International Conference on Engineering Applications of Neural Networks

EANN 2017: Engineering Applications of Neural Networks pp 99–110Cite as

Intrinsic Plagiarism Detection with Feature-Rich Imbalanced Dataset Learning

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

In the context of intrinsic plagiarism detection, we are trying to discover plagiarised passages in a text, based on the stylistic changes and inconsistencies within the document itself. The main idea consists in profiling the style of the original author and marking as outliers the passages that seem to differ significantly. Besides some novel stylistic and semantic features, the present work proposes a new approach to the problem, where machine learning plays a significant role. Notably, we also consider, for the first time, the reality of unbalanced training dataset in intrinsic plagiarism detection as a major parameter of the problem. Our detection system is tested on the data corpora of PAN Webis intrinsic plagiarism detection’s shared tasks of 2009 and 2011 and is compared to the results of the highest score participations.

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Notes

  1. 1.

    http://pan.webis.de/.

  2. 2.

    http://www.gutenberg.org.

  3. 3.

    https://opennlp.apache.org/.

  4. 4.

    http://scikit-learn.org.

References

  1. Stein, B., Lipka, N., Prettenhofer, P.: Intrinsic plagiarism analysis. Lang. Resour. Eval. 45(1), 63–82 (2010)

    Article  Google Scholar 

  2. Cheng, N., Chandramouli, R., Subbalakshmi, K.: Author gender identification from text. Digit. Invest. 8, 78–88 (2011)

    Article  Google Scholar 

  3. Stamatatos, E.: Intrinsic plagiarism detection using character n-gram profiles. In: Proceedings of the 3rd International Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (2009)

    Google Scholar 

  4. Oberreuter, G., L’Huillier, G., Rios, S.A., Velasquez, J.D.: Approaches for intrinsic and external plagiarism detection. In: Notebook for PAN at CLEF (2011)

    Google Scholar 

  5. Kestemont, M., Luyckx, K., Daelemans, W.: Intrinsic plagiarism detection using character trigrams distance scores. In: Notebook for PAN at CLEF (2011)

    Google Scholar 

  6. Kuznetsov, M., Motrenko, A., Kuznetsova, R., Strijov, V.: Methods for intrinsic plagiarism detection and author diarization. In: Notebook for PAN at CLEF (2016)

    Google Scholar 

  7. Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 1st International Competition on Plagiarism Detection (2009)

    Google Scholar 

  8. Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd International Competition on Plagiarism Detection (2011)

    Google Scholar 

  9. Alzahrani, S.M., Salim, N., Abraham, A.: Understanding plagiarism linguistic patterns, textual features, and detection methods. Int. J. Educ. Integr. 9(1), 55–71 (2013)

    Google Scholar 

  10. Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.: SVMs modeling for highly imbalanced classification. In: Journal of LaTeX Class Files (2002)

    Google Scholar 

  11. Rosso, P., Rangel, F., Potthast, M., Stamatatos, E., Tschuggnall, M., Stein, B.: Overview of PAN 2016 new challenges for authorship analysis: cross-genre profiling, clustering, diarization, and obfuscation. In: 7th International Conference of the CLEF Initiative (2016)

    Google Scholar 

  12. DuBay, W.H.: The Principles of Readability, p. 21 (2004)

    Google Scholar 

  13. Leanne, S., Matwin, S.: Intrinsic plagiarism detection using complexity analysis (2009)

    Google Scholar 

  14. Meyer zu Eissen, S., Stein, B.: Intrinsic plagiarism detection. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 565–569. Springer, Heidelberg (2006). doi:10.1007/11735106_66

    Chapter  Google Scholar 

  15. Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)

    MATH  Google Scholar 

  16. Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(1), 559–563 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Systems, Content and Interaction Laboratory, National Technical University of Athens, Athens, Greece

    Andrianna Polydouri, Georgios Siolas & Andreas Stafylopatis

Authors
  1. Andrianna Polydouri
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  2. Georgios Siolas
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  3. Andreas Stafylopatis
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Corresponding author

Correspondence to Andrianna Polydouri .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Polydouri, A., Siolas, G., Stafylopatis, A. (2017). Intrinsic Plagiarism Detection with Feature-Rich Imbalanced Dataset Learning. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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