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- W4380632125 abstract "The portrayal of someone else’s original thoughts or work as one’s own without giving the author credit is known as plagiarism. A review revealed that the COVID epidemic, which nearly brought the world to an end, had a significant impact on the quality of academic work published. Of the 310 publications in infected journals that were examined, 41.6% were found to be plagiarized. Additionally, it was noted that technology was the cause of it. In order to compare the similarities of two articles while maintaining contextual value throughout and not just merely using words, this paper focuses on detecting plagiarism in content between two articles using Transformer models and unsupervised algorithms for community detection methodologies. This protects not only the presentation but also the ideas in the original content. The basic inputs to the proposed system are any two articles, into each of which the entire pipeline is fitted. The outputs are then used to determine the degree of plagiarism between the articles. Word embeddings are created using the BERT transformer model, and communities inside the embeddings are found using the Louvain Community discovery technique. A determination of plagiarism is made using the score of the number of communities existing in the two articles." @default.
- W4380632125 created "2023-06-15" @default.
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- W4380632125 date "2023-01-01" @default.
- W4380632125 modified "2023-09-23" @default.
- W4380632125 title "Detection of Plagiarism in Contextual Meaning Using Transformer Model and Community Detection Algorithm" @default.
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- W4380632125 doi "https://doi.org/10.1007/978-981-99-0838-7_67" @default.
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