{"title":"Cross-Language Plagiarism Detection Model Based On Multiple Features","authors":"Gang Liu, Yichao Dong, Guang Li","doi":"10.1109/ISCC53001.2021.9631406","DOIUrl":null,"url":null,"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.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium on Computers and Communications (ISCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC53001.2021.9631406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.