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- W4200534306 abstract "As information sharing becomes more and more convenient, a lot of phenomena of plagiarism shows up. The study of cross-language plagiarism is an important problem that the whole academic circle tries to solve it collectively. In this paper, a multiple-features based cross-language plagiarism detection model is proposed, which includes cross-language plagiarism candidate retrieval based on multiple features and cross-language plagiarism detection based on dynamic text alignment. For cross-language plagiarism candidate retrieval, it is mainly based on the translation features. What's more, for cross-language plagiarism detection, a text-alignment based similarity analysis was used to filter the final results between the identified paragraphs. In this step, our approach doesn't use a machine translation system to convert longer text, but uses a dictionary to obtain the translation of a single word. Moreover, experimental results show that our method outperforms the previous methods and achieved the best results in four datasets." @default.
- W4200534306 created "2021-12-31" @default.
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- W4200534306 creator A5058637832 @default.
- W4200534306 date "2021-09-05" @default.
- W4200534306 modified "2023-09-29" @default.
- W4200534306 title "Cross-Language Plagiarism Detection Model Based On Multiple Features" @default.
- W4200534306 doi "https://doi.org/10.1109/iscc53001.2021.9631406" @default.
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