PlagCheck: An efficient way to identify Plagiarism using BERT
Plagiarism is stealing someone’s ideas and presenting them as yours. University students’ use of plagiarism is a serious problem that compromises their preparation and the university’s attempts to produce qualified graduates. Universities attempt to combat this issue with strong ethics regulations, but in order to put these policies into effect; they need the appropriate plagiarism detection tools at reasonable prices. In this article, we introduce PlagCheck, a high-volume, quick, and affordable plagiarism detection system created using the word embeddingsmodel and intended for use on text-based student assignments (essays, theses, homework). We go over the benefits of this approach in terms of cost, accuracy, and speed. This software will help educators and reduce their workload by helping them check plagiarism for each student’s assignment quickly and efficiently.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Anu Saini, Ankita Bahl, S. K. M. S. 2016. Plagiarism checker: Text mining. International Journal of Computer Applications (0975 – 8887) Vol.134, No.3. DOI: https://doi.org/10.5120/ijca2016907833
- Congcong Wang, Paul Nulty, D. L. 2020. A comparative study on word embeddings in deep learning for text classification. In Conference: NLPIR 2020: 4th International Conference on Natural Language Processing and Information Retrieval. 37–46. DOI: https://doi.org/10.1145/3443279.3443304
- Dhanasekar Sundararaman, Vivek Subramanian, G. W. S. S. D. S. D. W. L. C. 2019. Syntax-infused transformer and bert models for machine translation and natural language understanding.
- D.S. Adane, Abhishek Angale, A. S. R. A. S. Y. 2022. Plagiarism detection in programming using performance analyzing features. In International Journal of Next-Generation Computing. Vol. vol.13(5). DOI: https://doi.org/10.47164/ijngc.v13i5.964
- Faisal Rahutomo, Teruaki Kitasuka, M. A. 2012. Semantic cosine similarity. In Conference: The 7th International Student Conference on Advanced Science and Technology ICAST 2012At: Seoul, South Korea.
- Jacob Devlin, Ming-Wei Chang, K. L. K. T. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Vol. 1. Association for Computational Linguistics, 4171–4186. Jeffrey Pennington, Richard Socher, C. D. M. 2014.
- Glove: Global vectors for word representation. In Conference: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
- Tom Kenter, M. d. R. 2015. Short text similarity with word embeddings. In CIKM ’15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 1411–1420. DOI: https://doi.org/10.1145/2806416.2806475
- Tomas Mikolov, Kai Chen, G. C. J. D. 2013. Efficient estimation of word representations in vector space