Advertisement

Towards Document Plagiarism Detection Based on the Relevance and Fragmentation of the Reused Text

  • Fernando Sánchez-Vega
  • Luis Villaseñor-Pineda
  • Manuel Montes-y-Gómez
  • Paolo Rosso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6437)

Abstract

Traditionally, External Plagiarism Detection has been carried out by determining and measuring the similar sections between a given pair of documents, known as source and suspicious documents. One of the main difficulties of this task resides on the fact that not all similar text sections are examples of plagiarism, since thematic coincidences also tend to produce portions of common text. In order to face this problem in this paper we propose to represent the common (possibly reused) text by means of a set features that denote its relevance and fragmentation. This new representation, used in conjunction with supervised learning algorithms, provides more elements for the automatic detection of document plagiarism; in particular, our experimental results show that it clearly outperformed the accuracy results achieved by traditional n-gram based approaches.

Keywords

Plagiarism detection text reuse supervised classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barrón-Cedeño, A., Rosso, P.: On Automatic Plagiarism Detection Based on n-grams Comparison. In: Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval (ECIR), Berlin, Heidelberg (2009)Google Scholar
  2. 2.
    Basile, C., Benedetto, D., Caglioti, E., Cristadoro, G., Degli Esposti, M.: A Plagiarism Detection Procedure in Three Steps: Selection, Matches and “Squares”. In: Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN 2009), Donostia-San Sebastian, Spain, pp. 1–9 (September 2009)Google Scholar
  3. 3.
    Clough, P.: Old and new challenges in automatic plagiarism detection. National Plagiarism Advisory Service 76 (2003)Google Scholar
  4. 4.
    Clough, P., Gaizauskas, R., Piao, S., Wilks, Y.: METER: Measuring Text Reuse. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia (2002)Google Scholar
  5. 5.
    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 (2001)Google Scholar
  6. 6.
    Grozea, C., Gehl, C., Popescu, M.: ENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection. In: Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN 2009), Donostia-San Sebastian, Spain, pp. 1–9 (September 2009)Google Scholar
  7. 7.
    Kasprzak, J., Brandejs, M., Křipač, M.: Finding Plagiarism by Evaluating Document Similarities. In: Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN 2009), Donostia-San Sebastian, Spain, pp. 1–9 (September 2009)Google Scholar
  8. 8.
    Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.: Overview of the 1st International Competition on Plagiarism Detection. In: Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN 2009), Donostia-San Sebastian, Spain, pp. 1–9 (September 2009)Google Scholar
  9. 9.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comp. Surv. 34(1) (2002)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier, Amsterdam (2005)zbMATHGoogle Scholar
  11. 11.
    Zechner, M., Muhr, M., Kern, R., Granitzer, M.: External and Intrinsic Plagiarism Detection using Vector Space Models. In: Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse (PAN 2009), Donostia-San Sebastian, Spain, pp. 1–9 (September 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fernando Sánchez-Vega
    • 1
  • Luis Villaseñor-Pineda
    • 1
  • Manuel Montes-y-Gómez
    • 1
    • 3
  • Paolo Rosso
    • 2
  1. 1.Laboratory of Language Technologies, Department of Computational SciencesNational Institute of Astrophysics, Optics and Electronics (INAOE)Mexico
  2. 2.Natural Language Engineering Lab, ELiRF, DSICUniversidad Politécnica de ValenciaSpain
  3. 3.Department of Computer and Information SciencesUniversity of AlabamaBirminghamMexico

Personalised recommendations