Text-Mining Research Group

Plagiarism Detection based on Singular Value Decomposition

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

Year: 2008

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Zdeněk Češka

E-mail: zceska@kiv.zcu.cz
WWW: http://www.kiv.zcu.cz/en/department/members/detail.html?login=zceska

Zdeněk has been working for various international companies in the field of Software Engineering. He has earned Master's Degree and PhD's Degree in the field of Computer Science and Engineering. His research interests include Mathematics & Algorithmization, Plagiarism Detection, Multilingual Processing, Text Classification, and other related fields.

Automatic Plagiarism Detection
Authors:	Zdeněk Češka
Desc.:	This project focuses on the particular field of automatic plagiarism detection in written text. The main principle of this project is the application of Latent Semantic Analysis in conjunction with word N-grams.