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Latent Dirichlet Allocation and POS Tags Based Method for External Plagiarism Detection: LDA and POS Tags Based Plagiarism Detection

Author

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  • Ali Daud

    (King Abdulaziz University, Jeddah, Saudi Arabia & International Islamic University, Islamabad, Pakistan)

  • Jamal Ahmad Khan

    (International Islamic University, Islamabad, Pakistan)

  • Jamal Abdul Nasir

    (International Islamic University, Islamabad, Pakistan)

  • Rabeeh Ayaz Abbasi

    (King Abdulaziz University, Jeddah, Saudi Arabia & Quaid-i-Azam University, Islamabad, Pakistan)

  • Naif Radi Aljohani

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

  • Jalal S. Alowibdi

    (Faculty of Computing and Information Technology, University of Jeddah, Jeddah, Saudi Arabia)

Abstract

In this article we present a new semantic and syntactic-based method for external plagiarism detection. In the proposed approach, latent dirichlet allocation (LDA) and parts of speech (POS) tags are used together to detect plagiarism between the sample and a number of source documents. The basic hypothesis is that considering semantic and syntactic information between two text documents may improve the performance of the plagiarism detection task. Our method is based on two steps, naming, which is a pre-processing where we detect the topics from the sentences in documents using the LDA and convert each sentence in POS tags array; then a post processing step where the suspicious cases are verified purely on the basis of semantic rules. For two types of external plagiarism (copy and random obfuscation), we empirically compare our approach to the state-of-the-art N-gram based and stop-word N-gram based methods and observe significant improvements.

Suggested Citation

  • Ali Daud & Jamal Ahmad Khan & Jamal Abdul Nasir & Rabeeh Ayaz Abbasi & Naif Radi Aljohani & Jalal S. Alowibdi, 2018. "Latent Dirichlet Allocation and POS Tags Based Method for External Plagiarism Detection: LDA and POS Tags Based Plagiarism Detection," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(3), pages 53-69, July.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:3:p:53-69
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    Cited by:

    1. Hong, Ming & Wang, Heyong, 2021. "Research on customer opinion summarization using topic mining and deep neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 88-114.

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