LEADER 03887nam 22006015 450 001 9910736031703321 005 20230731224336.0 010 $a9783658420628 010 $a3658420626 024 7 $a10.1007/978-3-658-42062-8 035 $a(MiAaPQ)EBC30670635 035 $a(Au-PeEL)EBL30670635 035 $a(DE-He213)978-3-658-42062-8 035 $a(PPN)272257583 035 $a(CKB)27899960300041 035 $a(OCoLC)1392344535 035 $a(EXLCZ)9927899960300041 100 $a20230731d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalyzing Non-Textual Content Elements to Detect Academic Plagiarism /$fby Norman Meuschke 205 $a1st ed. 2023. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2023. 215 $a1 online resource (290 pages) 311 08$aPrint version: Meuschke, Norman Analyzing Non-Textual Content Elements to Detect Academic Plagiarism Wiesbaden : Springer Fachmedien Wiesbaden GmbH,c2023 9783658420611 327 $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. 330 $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. 606 $aNatural language processing (Computer science) 606 $aImage processing?Digital techniques 606 $aComputer vision 606 $aPattern recognition systems 606 $aNatural Language Processing (NLP) 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aAutomated Pattern Recognition 615 0$aNatural language processing (Computer science) 615 0$aImage processing?Digital techniques. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 14$aNatural Language Processing (NLP). 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aAutomated Pattern Recognition. 676 $a808.025 700 $aMeuschke$b Norman$01380401 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736031703321 996 $aAnalyzing Non-Textual Content Elements to Detect Academic Plagiarism$93421662 997 $aUNINA