JCF: Joint Coarse and Fine-Grained Similarity Comparison for Plagiarism Detection Based on NLP
學年 111
學期 2
出版（發表）日期 2023-06-24
作品名稱 JCF: Joint Coarse and Fine-Grained Similarity Comparison for Plagiarism Detection Based on NLP
作品名稱（其他語言）
著者 C. Y. Chang; S.-J. Jhang; S.-J. Wu; D. S. Roy
單位
出版者
著錄名稱、卷期、頁數 Journal of Supercomputing 80, p.363-394
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.
關鍵字 Natural language processing;TF–IDF;Word2Vec;Coarse and fine grained;Document similarity
語言 en_US
ISSN 1573-0484; 0920-8542
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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