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- Author: Bela Gipp
- Publisher: Springer Vieweg
- Year: 2014
- Pages: 350
- ISBN-10: 3658063939
- ISBN-13: 9783658063931
- Format: 14.8 x 21 x 2.1 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
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- Author: Bela Gipp
- Publisher: Springer Vieweg
- Year: 2014
- Pages: 350
- ISBN-10: 3658063939
- ISBN-13: 9783658063931
- Format: 14.8 x 21 x 2.1 cm, minkšti viršeliai
- Language: English English
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.
Reviews