Abstract
Currently, many studies are measuring the similarity between documents in a specific language, such as Vietnamese - Vietnamese and English - English. However, situations have recently appeared in the problem of copying articles. For example, English sources have been translated into Vietnamese and edited into their manuscripts. As a result, it is considered cross-language plagiarism. Therefore, this study has applied a new approach: translate from English to Vietnamese documents, then calculate and compare the translated document with documents modified or copied from a translated document. In the study, the main focus is on stages such as Translating English documents into Vietnamese, preprocessing documents, and determining the similarity between documents. The determination of similarity between documents mentioned in this topic is Cosine similarity based on Term Frequency (TF), Inverse Document Frequency (IDF), and word order similarity in the text. Combine these two metrics to give a similar result that is more accurate and convincing. The data is collected in 7 topics with related topics with the number of 15 documents with lengths from 2000 to more than 8000 words, successfully built a document translation integration system based on Google Translate Application Programming Interface (API) and similarity checking, Precision and Recall measures show very positive results over 80%.
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References
Dien, T.T., Han, H.N., Thai-Nghe, N.: An approach for plagiarism detection in learning resources. In: Dang, T.K., Küng, J., Takizawa, M., Bui, S.H. (eds.) FDSE 2019. LNCS, vol. 11814, pp. 722–730. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35653-8_52
Chan, N.N., Roussanaly, A., Boyer, A.: Learning resource recommendation: an orchestration of content-based filtering, word semantic similarity and page ranking. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds.) EC-TEL 2014. LNCS, vol. 8719, pp. 302–316. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11200-8_23
Yunanda, G., Nurjanah, D., Meliana, S.: Recommendation system from microsoft news data using tf-idf and cosine similarity methods. Build. Inf. Technol. Sci. 4(1), 277–284 (2022). http://ejurnal.seminar-id.com/index.php/bits/article/view/1670
Renuka, S., Raj Kiran, G.S.S., Rohit, P.: An unsupervised content-based article recommendation system using natural language processing. In: Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S., Falkowski-Gilski, P. (eds.) Data Intelligence and Cognitive Informatics. AIS, pp. 165–180. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-8530-2_13
Bagul, D.V., Barve, S.: A novel content-based recommendation approach based on lda topic modeling for literature recommendation. In: 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 954–961 (2021)
Resta, O.A., Aditya, A., Purwiantono, F.E.: Plagiarism detection in students’ theses using the cosine similarity method. Sinkron : jurnal dan penelitian teknik informatika 5(2), 305–313 (2021). https://polgan.ac.id/jurnal/index.php/sinkron/article/view/10909
Chavan, H., Taufik, M., Kadave, R., Chandra, N.: Plagiarism detector using machine learning. Int. J. Res. Eng. Sci. Manag. 4(4), 152–154 (2021). http://journals.resaim.com/ijresm/article/view/677
Fauzi, R., Iqbal, M., Haryanti, T.: Design and implementation of a final project plagiarism detection system using cosine similarity method. IJAIT (Int. J. Appl. Inf. Technol.), 1–16 (2022). https://journals.telkomuniversity.ac.id/ijait/article/view/4146
Ehsan, N., Shakery, A.: Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information. Inf. Process. Manag. 52(6), 1004–1017 (2016). https://www.sciencedirect.com/science/article/abs/pii/S0306457316300784
Gomaa, W., Fahmy, A.: A survey of text similarity approaches. Int. J. Comput. Appl. 68 (2013)
Roostaee, M., Sadreddini, M.H., Fakhrahmad, S.M.: An effective approach to candidate retrieval for cross-language plagiarism detection: a fusion of conceptual and keyword-based schemes. Inf. Process. Manag. 57(2), 102150 (2020). https://www.sciencedirect.com/science/article/abs/pii/S0306457318310148
Feng, G., et al.: Question classification by approximating semantics. In: Proceedings of the 24th International Conference on World Wide Web, pp. 407–417 (2015)
Ceska, Z., Toman, M., Jezek, K.: Multilingual plagiarism detection. In: Dochev, D., Pistore, M., Traverso, P. (eds.) AIMSA 2008. LNCS (LNAI), vol. 5253, pp. 83–92. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85776-1_8
Juričić, V., Štefanec, V., Bosanac, S.: Multilingual plagiarism detection corpus. In: 2012 Proceedings of the 35th International Convention MIPRO, pp. 1310–1314 (2012)
Dougherty, M.V.: Translation plagiarism. In: Disguised Academic Plagiarism. REF, vol. 8, pp. 13–36. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46711-1_2
Yankova, D.: On translated plagiarism in academic discourse. Engl. Stud. NBU 6, 189–200 (2020)
Wiwanitkit, V.: How to verify and manage the translational plagiarism? Open Access Macedonian J. Med. Sci. 4 (2016)
Pataki, M., Marosi, A.: Searching for translated plagiarism with the help of desktop grids. J. Grid Comput. 11 (2013)
Mustofa, K., Sir, Y.A.: Early-detection system for cross-language (translated) plagiarism. In: Mustofa, K., Neuhold, E.J., Tjoa, A.M., Weippl, E., You, I. (eds.) ICT-EurAsia 2013. LNCS, vol. 7804, pp. 21–30. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36818-9_3
Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Knowl.-Based Syst. 45, 45–62 (2011)
Barrón-Cedeño, A., Rosso, P.: Methods for cross-language plagiarism detection. Knowl.-Based Syst. 50, 211–217 (2013)
Alabbas, M., Khudeyer, R.S., Radif, M., Hameed, H.K.: Online multilingual plagiarism detection system using multi search engines. J. Southwest Jiaotong Univ. 54(6) (2019). https://doi.org/10.35741/issn.0258-2724.54.6.30
Anguita, A., Beghelli, A., Creixell, W.: Automatic cross-language plagiarism detection. In: 2011 7th International Conference on Natural Language Processing and Knowledge Engineering, pp. 173–176 (2011)
Duong, T.L.: Research on text similarity in Vietnamese and its application to support the assessment of copying electronic articles. Hanoi Open University, Hanoi (2014)
Sanderson, M., Zobel, J.: Information retrieval system evaluation: effort, sensitivity, and reliability. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2005, pp. 162–169 (2005)
Arora, M., Kanjilal, U., Varshney, D.: Evaluation of information retrieval: precision and recall. Int. J. Indian Cult. Bus. Manag. 12, 224 (2016)
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This study is funded in part by the Can Tho University, Code: TDH2022-04.
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Nguyen, H.T., Le, A.D., Thai-Nghe, N., Dien, T.T. (2022). An Approach for Similarity Vietnamese Documents Detection from English Documents. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_39
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DOI: https://doi.org/10.1007/978-981-19-8069-5_39
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