An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Muna Alsallal, Rahat Iqbal, Anne James, Saad Amin, Vasile Palade

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.
Original languageEnglish
DOIs
Publication statusPublished - 1 Sep 2016
Event9th International Conference on Developments in eSystems Engineering - Liverpool and Leeds, United Kingdom
Duration: 31 Aug 20162 Sep 2016
http://dese.org.uk/dese2016-conference/

Conference

Conference9th International Conference on Developments in eSystems Engineering
Abbreviated titleDeSE
CountryUnited Kingdom
CityLiverpool and Leeds
Period31/08/162/09/16
Internet address

Keywords

  • Plagiarism
  • Semantics
  • Feature extraction
  • Tools
  • Support vector machines
  • Education
  • Writing

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Alsallal, M., Iqbal, R., James, A., Amin, S., & Palade, V. (2016). An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection. Paper presented at 9th International Conference on Developments in eSystems Engineering, Liverpool and Leeds, United Kingdom. https://doi.org/10.1109/DeSE.2016.1