Abstract
The growth of textual content in various languages and the advancement of automatic translation systems has led to an increase of cases of translated plagiarism. When a text is translated into another language, word order will change and words may be substituted by synonyms, and as a result detection will be more challenging. The purpose of this paper is to introduce a new technique for English-Arabic cross-language plagiarism detection. This method combines word embedding, term weighting techniques, and universal sentence encoder models, in order to improve detection of sentence similarity. The proposed model has been evaluated based on English-Arabic cross-lingual datasets, and experimental results show improved performance when compared with other Arabic-English cross-lingual evaluation methods presented at SemEval-2017.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alaa, Z., Tiun, S., Abdulameer, M.: Cross-language plagiarism of Arabic-English documents using linear logistic regression. J. Theor. Appl. Inf. Technol. 83(1), 20–33 (2016)
Aljohani, A., Mohd, M.: Arabic-English cross-language plagiarism detection using winnowing algorithm. Inf. Technol. J. 13(14), 23–49 (2014)
Alzahrani, S.M., Salim, N., Abraham, A.: Understanding plagiarism linguistic patterns, textual features, and detection methods. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(2), 133–149 (2011)
Barrón-Cedeno, A., Rosso, P., Pinto, D., Juan, A.: On cross-lingual plagiarism analysis using a statistical model. In: PAN, pp. 1–10 (2008)
Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: Semeval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 1–14 (2017)
Eisa, T.A.E., Salim, N., Alzahrani, S.: Existing plagiarism detection techniques: a systematic mapping of the scholarly literature. Online Inf. Rev. 39(3), 383–400 (2015)
Ezzikouri, H., Oukessou, M., Youness, M., Erritali, M.: Fuzzy cross language plagiarism detection (Arabic-English) using WordNet in a big data environment. In: 2nd International Conference on Cloud and Big Data Computing, pp. 22–27. ACM (2018)
Gipp, B.: Citation-based Plagiarism Detection, pp. 57–88. Springer, Wiesbaden (2014). https://doi.org/10.1007/978-3-658-06394-8_4
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)
Hattab, E.: Cross-language plagiarism detection method: arabic vs. english. In: 2015 International Conference on Developments of E-Systems Engineering (DeSE), pp. 141–144. IEEE (2015)
Lioma, C., Blanco, R.: Part of speech based term weighting for information retrieval. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 412–423. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00958-7_37
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: 26th International Conference on Neural Information Processing Systems, vol. 2, pp. 3111–3119 (2013)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)
Shao, Y.: HCTI at SemEval-2017 Task 1: use convolutional neural network to evaluate semantic textual similarity. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, pp. 130–133 (2017)
Tian, J., Zhou, Z., Lan, M., Wu, Y.: ECNU at SemEval-2017 task 1: leverage kernel-based traditional NLP features and neural networks to build a universal model for multilingual and cross-lingual semantic textual similarity. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, pp. 191–197 (2017)
Wu, H., Huang, H.Y., Jian, P., Guo, Y., Su, C.: BIT at SemEval-2017 task 1: using semantic information space to evaluate semantic textual similarity. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Vancouver, pp. 77–84 (2017)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on Association for Computational Linguistics, pp. 133–138 (1994)
Yang, Y., et al.: Multilingual universal sentence encoder for semantic retrieval. arXiv preprint arXiv:1907.04307 [CS.CL] (2019)
Meyer zu Eissen, S., Stein, B., Kulig, M.: Plagiarism detection without reference collections. In: Decker, R., Lenz, H.-J. (eds.) Advances in Data Analysis. SCDAKO, pp. 359–366. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70981-7_40
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Alotaibi, N., Joy, M. (2020). Using Sentence Embedding for Cross-Language Plagiarism Detection. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVII. SGAI 2020. Lecture Notes in Computer Science(), vol 12498. Springer, Cham. https://doi.org/10.1007/978-3-030-63799-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-63799-6_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63798-9
Online ISBN: 978-3-030-63799-6
eBook Packages: Computer ScienceComputer Science (R0)