Abstract
Plagiarism is specifically defined as literary theft of paragraphs or sentences from unreferenced source. This unauthorized behavior is a real problem that targets scientific research scope. This paper proposes a Hybrid Arabic Plagiarism Detection System (HYPLAG). The HYPLAG approach combines corpus-based and knowledge-based approaches by utilizing an Arabic semantic resource (Arabic WordNet). A preliminary study on texts from undergraduate students was conducted to understand their behavior and the patterns used in plagiarism. The results of the study show that students apply different techniques to plagiarized sentences, also it shows changes in sentence’s components (verbs, nouns, and adjectives). HYPLAG was evaluated on the ExAraPlagDet-2015 dataset against several other approaches that participated in the AraPlagDet PAN@FIRE shared task on Extrinsic Arabic plagiarism detection obtaining a higher performance (F-score 89% vs. 84% obtained by the best performing system at AraPlagDet) with less computational time.
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Notes
- 1.
http://www.plagiarism.org/resources/facts-and-stats/; accessed on October 2016.
- 2.
http://www.checkforplagiarism.net/cyber-plagiarism; accessed on October 2016.
- 3.
https://infogr.am/Plagiarism-606324; accessed on October 2016.
- 4.
We used Farasa NER tool for named entity recognition, http://qatsdemo.cloudapp.net/farasa/; accessed on December 2016.
- 5.
http://globalwordnet.org/arabic-wordnet/; accessed on November 2016.
- 6.
http://misc-umc.org/AraPlagDet/?i=1; accessed on September 2016.
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Acknowledgment
The work of Paolo Rosso was funded by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.
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Ghanem, B., Arafeh, L., Rosso, P., Sánchez-Vega, F. (2018). HYPLAG: Hybrid Arabic Text Plagiarism Detection System. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_33
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