JCF: Joint Coarse and Fine-Grained Similarity Comparison for Plagiarism Detection Based on NLP

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题名:	JCF: Joint Coarse and Fine-Grained Similarity Comparison for Plagiarism Detection Based on NLP
作者:	Chang, C. Y.;Jhang, S.-J.;Wu, S.-J.;Roy, D. S.
关键词:	Natural language processing;TF–IDF;Word2Vec;Coarse and fine grained;Document similarity
日期:	2023-06-24
上传时间:	2024-03-07 12:05:58 (UTC+8)
出版者:	Springer New York LLC
摘要:	Document similarity recognition is one of the most important problems in natural language processing. This paper proposes a plagiarism comparison mechanism called JCF. Initially, the TF–IDF scheme is applied to build a bag of words as the representation of the common features of all documents. Then, the plagiarism comparison is carried out in a coarse-grained manner, which speeds up the similarity comparison. Finally, the most similar documents can then be compared in detail based on a fine-grained approach. In addition, the JCF detects plagiarism at both syntax level and semantic-like level. To prevent the distortion of similarity comparison, this paper further develops a similarity restoration approach such that the proposed JCF can obtain both advantages of quickness and accuracy. Performance studies confirm that the proposed JCF outperforms existing studies in terms of precision, recall and F1 score.
關聯:	Journal of Supercomputing 80, p.363-394
DOI:	10.1007/s11227-023-05472-0
显示于类别:	[資訊工程學系暨研究所] 期刊論文

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