TEIMMA: The First Content Reuse Annotator for Text, Images, and Math. Satpute, A., Greiner-Petter, A., Schubotz, M., Meuschke, N., Aizawa, A., & Gipp, B. In Proceedings of 23rd Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL), Santa Fe, NM, USA, 2023.
TEIMMA: The First Content Reuse Annotator for Text, Images, and Math [pdf]Paper  TEIMMA: The First Content Reuse Annotator for Text, Images, and Math [link]Demo  doi  abstract   bibtex   2 downloads  
This demo paper presents the first tool to annotate the reuse of text, images, and mathematical formulae in a document pair—TEIMMA. Annotating content reuse is particularly useful to develop plagiarism detection algorithms. Real-world content reuse is often obfuscated, which makes it challenging to identify such cases. TEIMMA allows entering the obfuscation type to enable novel classifications for confirmed cases of plagiarism. It enables recording different reuse types for text, images, and mathematical formulae in HTML and supports users by visualizing the content reuse in a document pair using similarity detection methods for text and math.

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