000 | 03564nam a22004935i 4500 | ||
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001 | 978-3-658-42062-8 | ||
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_a9783658420628 _9978-3-658-42062-8 |
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_a10.1007/978-3-658-42062-8 _2doi |
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_a006.35 _223 |
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_aMeuschke, Norman. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aAnalyzing Non-Textual Content Elements to Detect Academic Plagiarism _h[electronic resource] / _cby Norman Meuschke. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aWiesbaden : _bSpringer Fachmedien Wiesbaden : _bImprint: Springer Vieweg, _c2023. |
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300 |
_aXXIII, 272 p. 55 illus. Textbook for German language market. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aIntroduction -- Academic Plagiarism Detection -- Citation-based Plagiarism Detection -- Image-based Plagiarism Detection -- Math-based Plagiarism Detection -- Hybrid Plagiarism Detection System -- Conclusion and Future Work -- References. | |
520 | _aIdentifying plagiarism is a pressing problem for research institutions, publishers, and funding bodies. Current detection methods focus on textual analysis and find copied, moderately reworded, or translated content. However, detecting more subtle forms of plagiarism, including strong paraphrasing, sense-for-sense translations, or the reuse of non-textual content and ideas, remains a challenge. This book presents a novel approach to address this problem—analyzing non-textual elements in academic documents, such as citations, images, and mathematical content. The proposed detection techniques are validated in five evaluations using confirmed plagiarism cases and exploratory searches for new instances. The results show that non-textual elements contain much semantic information, are language-independent, and resilient to typical tactics for concealing plagiarism. Incorporating non-textual content analysis complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of plagiarism. The book introduces the first integrated plagiarism detection system that combines citation, image, math, and text similarity analysis. Its user interface features visual aids that significantly reduce the time and effort users must invest in examining content similarity. About the author Norman Meuschke is a Senior Researcher for Information Retrieval and Natural Language Processing at the University of Göttingen, Germany. | ||
650 | 0 | _aNatural language processing (Computer science). | |
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aPattern recognition systems. | |
650 | 1 | 4 | _aNatural Language Processing (NLP). |
650 | 2 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
650 | 2 | 4 | _aAutomated Pattern Recognition. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783658420611 |
776 | 0 | 8 |
_iPrinted edition: _z9783658420635 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-658-42062-8 |
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