Detecting Plagiarism in Text Documents.
ABSTRACT Plagiarism aims at identifying the amount of information that is copied or reproduced in modified representation of original
documents. This is quiet common among students, researchers and academicians that leads to a kind of unrecognizing. Though
there exits some commercial tools to detect plagiarism, still plagiarism is tricky and quiet challenging task due to abundant
information available online. Commercially existing softwares adopt methods like paraphrasing, sentence matching or keyword
matching. This paper focuses its attention on identifying some key parameters that would help to identify plagiarism in a
better manner and to report plagiarism in an effective way. The result seems to be promising and have further scope in detecting
- SourceAvailable from: Bela Gipp01/2013; University of Magdeburg.
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ABSTRACT: Publication in the field of technical sciences Plagiarism is a problem with far-reaching consequences for the sciences. However, even today’s best software-based systems can only reliably identify copy&paste plagiarism. Disguised plagiarism forms, including paraphrased text, cross-language plagiarism, as well as structural and idea plagiarism often remain undetected. This weakness of current systems results in a large percentage of scientific plagiarism going undetected. Bela Gipp provides an overview of the state-of-the art in plagiarism detection and an analysis of why these approaches fail to detect disguised plagiarism forms. The author proposes Citation-based Plagiarism Detection to address this shortcoming. Unlike character-based approaches, this approach does not rely on text comparisons alone, but analyzes citation patterns within documents to form a language-independent "semantic fingerprint" for similarity assessment. The practicability of Citation-based Plagiarism Detection was proven by its capability to identify so-far non-machine detectable plagiarism in scientific publications. Contents Current state of plagiarism detection approaches and systems Citation-based Plagiarism Detection Target Groups Readers interested in the problem of plagiarism in the sciences Faculty and students from all disciplines, but especially computer science The Author Bela Gipp is a postdoctoral researcher at the University of California, Berkeley. Content Level » Research Keywords » Citation analysis - Citation-based Plagiarism Detection - Detection approaches - Plagiarism detection - Plagiarism forms01/2014: pages 354; Springer Vieweg Research., ISBN: 978-3-658-06393-1