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Paraphrase plagiarism identification with character-level features

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Abstract

Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identification consists in automatically recognizing document fragments that contain reused text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes and morphological or lexical substitutions. Our main hypothesis establishes that the original author’s writing style fingerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identification, which represents a new valuable resource to the NLP community for future research work in this field.

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Notes

  1. The PAN competition (http://pan.webis.de)

  2. The P4P corpus and guidelines used for its annotation are available at http://clic.ub.edu/corpus/en/paraphrases-en

  3. A large lexical database of English (https://wordnet.princeton.edu/).

  4. http://www.uni-weimar.de/en/media/chairs/webis/corpora/pan-pc-10/#webis-download

  5. Workers from the Amazon Mechanical Turk (https://www.mturk.com/mturk/welcome) perform simple tasks in exchange for a monetary reward.

  6. The second categorization level consists of four subclasses and two classes without subclasses

  7. http://ccc.inaoep.mx/~mmontesg/resources/corpusP4PIN.zip

  8. We used the implementation provided by Weka (http://www.cs.waikato.ac.nz/ml/weka/)

  9. For this experiment, we keep the best configuration obtained using the Brown corpus.

  10. The LCS performance is constant since the n parameter is not applicable for it.

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Acknowledgements

This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030).

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Authors and Affiliations

  1. Computer Science Department, Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    Fernando Sánchez-Vega, Manuel Montes-y-Gómez & Luis Villaseñor-Pineda

  2. Information Technologies Department, Universidad Autónoma Metropolitana (UAM) Unidad Cuajimalpa, Ciudad de México, Mexico

    Esaú Villatoro-Tello

  3. PRHLT Research Center, Universitat Politècnica de València, València, Spain

    Paolo Rosso

  4. Department of Information and Communication Systems Engineering, University of the Aegean, Samos, Greece

    Efstathios Stamatatos

Authors
  1. Fernando Sánchez-Vega
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  2. Esaú Villatoro-Tello
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  3. Manuel Montes-y-Gómez
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  4. Paolo Rosso
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  5. Efstathios Stamatatos
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  6. Luis Villaseñor-Pineda
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Correspondence to Esaú Villatoro-Tello.

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Sánchez-Vega, F., Villatoro-Tello, E., Montes-y-Gómez, M. et al. Paraphrase plagiarism identification with character-level features. Pattern Anal Applic 22, 669–681 (2019). https://doi.org/10.1007/s10044-017-0674-z

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