Earticle

초록

영어

Providing effective methods of identification of high-obfuscation plagiarism seeds presents a significant research problem in the field of plagiarism detection. The conventional methods of plagiarism detection are based on single type of features to capture plagiarism seeds. But for high-obfuscation plagiarism detection, these single type features are not sufficient for identifying the plagiarism seeds effectively because of the varied plagiarism methods used in high-obfuscation plagiarism. This paper presents a multi-features fusion method for the high-obfuscation plagiarism seeds identification. This method exploits Logical Regression model to integrate lexicon features, syntax features, semantics features and structure features which extracted from suspicious document and source document. A multi-feature fusion classifier based on Logical Regression model is proposed to decide whether a text fragment pair can be regarded as plagiarism seeds or not. Experimental results on the PAN@CLEF2013 summary-obfuscation corpus show that the fusion of different types of features produces more accurate results.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Multi-features fusion via Logical Regression model
  3.1. Problem
  3.2. Multi-Features Fusion
  3.3. Features set for high-obfuscation plagiarism detection
 4. Result
  4.1. Corpus
  4.2. Measures
  4.3. Baseline
  4.4. Experimental results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Leilei Kong [ Harbin Engineering University, Heilongjiang Institute of Technology ]
• Zhimao Lu [ Harbin Engineering University ]
• Haoliang Qi [ Heilongjiang Institute of Technology ]
• Zhongyuan Han [ Heilongjiang Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605