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Using Sentence Similarity Measure for Plagiarism Detection of Arabic Documents

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

Abstract

Plagiarism detection it is a challenging task, particularly in natural language texts. Some plagiarism detection tools have been developed for diverse natural languages, especially English. In this paper, we propose, a new plagiarism detection system devoted to Arabic text documents. This system is based on an algorithm that uses a semantic sentence similarity measure. Indeed, the sentence similarity measure aggregates in a linear function between three components: the lexical-based LS including the common words, the semantic-based SS using the synonymy relationships, and the syntactico-semantic- based SSS semantic arguments properties notably semantic argument and thematic role. It measures the semantic similarity between words that play the same syntactic role. Concerning the word-based semantic similarity, an information content-based measure is used to estimate the SS degree between words by exploiting the LMF Arabic standardized dictionary ElMadar. The performance of the proposed system was confirmed through experiments with student thesis reports that promising capabilities in identifying literal and some types of intelligent plagiarism. We also demonstrate its advantages over other plagiarism detection tools, including Aplag.

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Authors and Affiliations

  1. MIRACL Laboratory, Sfax University, Sfax, Tunisia

    Wafa Wali, Bilel Gargouri & Abdelmajid Ben Hamadou

Authors
  1. Wafa Wali
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  2. Bilel Gargouri
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  3. Abdelmajid Ben Hamadou
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Corresponding author

Correspondence to Wafa Wali .

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Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Wali, W., Gargouri, B., Ben Hamadou, A. (2018). Using Sentence Similarity Measure for Plagiarism Detection of Arabic Documents. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

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