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Fuzzy Semantic Plagiarism Detection

  • Ahmed Hamza Osman
  • Naomie Salim
  • Yogan Jaya Kumar
  • Albaraa Abuobieda
Part of the Communications in Computer and Information Science book series (CCIS, volume 322)

Abstract

This paper introduces a plagiarism detection scheme based on a Fuzzy Inference System and Semantic Role Labeling (FIS-SRL). The proposed technique analyses and compares text based on a semantic allocation for each term inside the sentence. SRL offers significant advantages when generating arguments for each sentence semantically. Voting for each argument generated by the FIS in order to select important arguments is also another feature of the proposed method. It has been concluded that not all arguments in the text affect the plagiarism detection process. Therefore, only the most important arguments were selected by the FIS, and the results have been used in the similarity calculation process. Experimental tests have been applied on the PAN-PC-09 data set and the results shows that the proposed method exhibits a better performance than the available recent methods of plagiarism detection, in terms of Recall, Precision and F-measure.

Keywords

Plagiarism Detection Semantic Similarity Semantic Role Fuzzy Inference System Rule Reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmed Hamza Osman
    • 1
    • 2
  • Naomie Salim
    • 1
  • Yogan Jaya Kumar
    • 1
  • Albaraa Abuobieda
    • 1
    • 2
  1. 1.Faculty of Computer Science and Information SystemsUniversitiTeknologi MalaysiaSkudaiMalaysia
  2. 2.Faculty of Computer StudiesInternational University of AfricaKhartoumSudan

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