Plagiarism Detection Based on Singular Value Decomposition

  • Zdenek Ceska
Conference paper

DOI: 10.1007/978-3-540-85287-2_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5221)
Cite this paper as:
Ceska Z. (2008) Plagiarism Detection Based on Singular Value Decomposition. In: Nordström B., Ranta A. (eds) Advances in Natural Language Processing. Lecture Notes in Computer Science, vol 5221. Springer, Berlin, Heidelberg

Abstract

Plagiarism is a widely spread problem that is the main focus of interest these days. In this paper, we propose a new method solving associations of phrases contained in text documents. This method, called SVDPlag, employs Singular Value Decomposition (SVD) for this purpose. Further, we discuss other approaches to plagiarism detection and compare them with our method. To examine the efficiency of plagiarism detection methods, we used an experimental corpus of 950 text documents about politics, which were created from the standard CTK corpus. The experiments indicate that our approach significantly improves the accuracy of plagiarism detection and overcomes other methods.

Keywords

Plagiarism Copy Detection Natural Language Processing Phrases N-grams Singular Value Decomposition Latent Semantic Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zdenek Ceska
    • 1
  1. 1.Department of Computer Science and Engineering, Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

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