Meuschke Analyzing Non-Textual Content Elements to Detect Academic Plagiarism 9783658420611

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Автор: Meuschke
Название: Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
ISBN: 9783658420611
Издательство: Springer
Классификация:

Учебное оборудование и технологии, компьютерное обучение (CAL)

Естественный язык и машинный перевод

Распознавание образов

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ISBN-10: 3658420618
Обложка/Формат: Soft cover
Страницы: 272
Вес: 0.00 кг.
Дата издания: 15.08.2023
Язык: English
Издание: 1st ed. 2023
Иллюстрации: 55 illustrations, black and white; xxiii, 272 p. 55 illus. textbook for german language market.
Размер: 210 x 148
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Identifying 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.
Дополнительное описание: Introduction.- Academic Plagiarism Detection.- Citation-based Plagiarism Detection.- Image-based Plagiarism Detection.- Math-based Plagiarism Detection.- Hybrid Plagiarism Detection System.- Conclusion and Future Work.- References.

Analyzing Non-Textual Content Elements to Detect Academic Plagiarism, Meuschke