Meta Text Aligner: Text Alignment Based on Predicted Plagiarism Relation

  • Samira Abnar
  • Mostafa Dehghani
  • Azadeh Shakery
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)


Text alignment is one of the main steps of plagiarism detection in textual environments. Considering the pattern in distribution of the common semantic elements of the two given documents, different strategies may be suitable for this task. In this paper we assume that the obfuscation level, i.e the plagiarism type, is a function of the distribution of the common elements in the two documents. Based on this assumption, we propose Meta Text Aligner which predicts plagiarism relation of two given documents and employs the prediction results to select the best text alignment strategy. Thus, it will potentially perform better than the existing methods which use a same strategy for all cases. As indicated by the experiments, we have been able to classify document pairs based on plagiarism type with the precision of \(89\%\). Furthermore exploiting the predictions of the classifier for choosing the proper method or the optimal configuration for each type we have been able to improve the Plagdet score of the existing methods.


Meta Text Aligner Plagiarism type Text alignment Plagiarism detection Patterns of distribution of common elements 


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  1. 1.
    Abnar, S., Dehghani, M., Zamani, H., Shakery, A.: Expanded n-grams for semantic text alignment. In: Lab Report for PAN at CLEF (2014)Google Scholar
  2. 2.
    Barrón-Cedeño, A., Rosso, P., Benedí, J.-M.: Reducing the plagiarism detection search space on the basis of the kullback-leibler distance. In: Gelbukh, A. (ed.) CICLing 2009. LNCS, vol. 5449, pp. 523–534. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  3. 3.
    Brin, S., Davis, J., Garcia-Molina, H.: Copy detection mechanisms for digital documents. ACM SIGMOD Record 24, 398–409 (1995)CrossRefGoogle Scholar
  4. 4.
    Gaizauskas, R., Foster, J., Wilks, Y., Arundel, J., Clough, P., Piao, S.: The meter corpus: a corpus for analysing journalistic text reuse. In: Proceedings of the Corpus Linguistics 2001 Conference, pp. 214–223 (2001)Google Scholar
  5. 5.
    Glinos, D.: A hybrid architecture for plagiarism detection. In: Lab Report for PAN at CLEF (2014)Google Scholar
  6. 6.
    Palkovskii, Y., Belov, A.: Developing high-resolution universal multitype n-gram plagiarism detector. In: Lab Report for PAN at CLEF (2014)Google Scholar
  7. 7.
    Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th international competition on plagiarism detection. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) Working Notes Papers of the CLEF 2014 Evaluation Labs (2014)Google Scholar
  8. 8.
    Sanchez-Perez, M., Sidorov, G., Gelbukh, A.: A winning approach to text alignment for text reuse detection at pan 2014. In: Lab Report for PAN at CLEF (2014)Google Scholar
  9. 9.
    Shivakumar, N., Garcia-Molina, H.: Scam: A copy detection mechanism for digital documents, pp. 1–13 (1995)Google Scholar
  10. 10.
    Stein, B., zu Eissen, S.M., Potthast, M.: Strategies for retrieving plagiarized documents. In: Proceedings of SIGIR 2007, pp. 825–826. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Samira Abnar
    • 1
  • Mostafa Dehghani
    • 2
  • Azadeh Shakery
    • 1
  1. 1.School of ECE, College of EngineeringUniversity of TehranTehranIran
  2. 2.Institute for Logic, Language and ComputationUniversity of AmsterdamAmsterdamThe Netherlands

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