An improved plagiarism detection scheme based on semantic role labeling

  • Authors:

  • Ahmed Hamza Osman;Naomie Salim;Mohammed Salem Binwahlan;Rihab Alteeb;Albaraa Abuobieda

  • Affiliations:

  • Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems, Skudai, Johor, Malaysia and International University of Africa, Faculty of Computer Studies, Khartoum, Sudan;Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems, Skudai, Johor, Malaysia;Faculty of Applied Sciences, Hadhramout University of Science & Technology, Seiyun, Hadhramout, Yemen;Sudan University of Science and Technology, Faculty of Computer Science, Khartoum, Sudan;Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems, Skudai, Johor, Malaysia and International University of Africa, Faculty of Computer Studies, Khartoum, Sudan

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

Plagiarism occurs when the content is copied without permission or citation. One of the contributing factors is that many text documents on the internet are easily copied and accessed. This paper introduces a plagiarism detection technique based on the Semantic Role Labeling (SRL). The technique analyses and compares text based on the semantic allocation for each term inside the sentence. SRL is superior in generating arguments for each sentence semantically. Weighting for each argument generated by SRL to study its behaviour is also introduced in this paper. It was found that not all arguments affect the plagiarism detection process. In addition, experimental results on PAN-PC-09 data sets showed that our method significantly outperforms the modern methods for plagiarism detection in terms of Recall, Precision and F-measure.