• DocumentCode
    2286431
  • Title

    Original content extraction oriented to anti-plagiarism

  • Author

    Shen, Yang ; Cheng, Ming ; Yao, Xing ; Wei, Wei

  • Author_Institution
    Sch. of Inf. Manage., Wuhan Univ., Wuhan, China
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    17
  • Lastpage
    22
  • Abstract
    In order to reduce the impact of inclusion of citations and references during the detection of plagiarism in academic theses, and extract the original content, the author created three ways to extract original content and remove the citation: 1) Removal of normative citations by symbol features; 2) removal tacit citations by Bayesian method based on the minimum risk and thesis structure; 3) removal common knowledge base on domain public knowledge base. The research results show that during the extraction of original content, the precision decreases as the risk coefficient increases, while the recall rate increases with the risk coefficient. When the risk coefficient is 60, the whole performance achieves the optimum. Plagiarism detection after extracting the original content presents a fault rate decrease from 9.09% to 4.52%.
  • Keywords
    belief networks; citation analysis; information retrieval; Bayesian method; content extraction; normative citations removal; plagiarism detection; removal tacit citations; Conference management; Content management; Data mining; Engineering management; Knowledge management; Plagiarism; Prototypes; Risk management; Software libraries; Web pages; Beyes; citation removal; content extraction; plagiarism; thesis structure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2009. ICMSE 2009. International Conference on
  • Conference_Location
    Moscow
  • Print_ISBN
    978-1-4244-3970-6
  • Electronic_ISBN
    978-1-4244-3971-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2009.5317530
  • Filename
    5317530