Abstract
This chapter provides background information on academic plagiarism and reviews technical approaches to detect it. Section Chapter 1 derives a definition and typology of academic plagiarism that is suitable for the technical research focus of this thesis. Section 2.2 provides a holistic overview of the research on academic plagiarism to contextualize the technically focused research areas that the subsequent sections present in detail. Sections 2.3 and 2.4 systematically analyze the research on plagiarism detection methods and describe production-grade systems that implement some of the presented methods. Section 2.5 presents datasets usable for evaluating plagiarism detection technology. Furthermore, the section discusses comprehensive performance evaluations of plagiarism detection methods and systems to highlight their weaknesses and demonstrate the research gap this thesis addresses. Section 2.6 summarizes the findings of the literature review and thereby fulfills Research Task 1. Section 2.7 derives the research idea pursued in this thesis.
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Notes
- 1.
PAN is a series of annual competitions for evaluating PD technology (see Section 2.5.1, p. 49).
- 2.
Turnitin, a major provider of plagiarism detection software, states that 15,000 institutions in 150 countries use its service [510]
- 3.
Parallel corpora consist of texts in language A and the translations of the texts in language B. Comparable corpora consist of texts of the same type, e.g., news articles, or on the same topic written in different languages. The text are not translations of one another [269, p. 487].
- 4.
We use other identifiers than Potthast et al. to be consistent with other formulae in this thesis.
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Meuschke, N. (2023). Academic Plagiarism Detection. In: Analyzing Non-Textual Content Elements to Detect Academic Plagiarism. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-42062-8_2
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DOI: https://doi.org/10.1007/978-3-658-42062-8_2
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