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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.
Compra de libros
Citation-based plagiarism detection, Bela Gipp
- Idioma
- Publicado en
- 2014
- Encuadernación
- (Tapa blanda)
Métodos de pago
Nadie lo ha calificado todavía.
- Título
- Citation-based plagiarism detection
- Subtítulo
- Detecting Disguised and Cross-language Plagiarism using Citation Pattern Analysis
- Idioma
- Inglés
- Autores
- Bela Gipp
- Editorial
- Springer Vieweg
- Publicado en
- 2014
- Formato
- Tapa blanda
- Páginas
- 376
- ISBN10
- 3658063939
- ISBN13
- 9783658063931
- Serie
- Descripción
- 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.