Abstract
Plagiarism is the process of using the ideas of another without naming the source. Plagiarism is unacceptable and could be viewed as cheating and stealing. Plagiarism detection is necessary but complicated as it is often facing significant challenges given the large amount of material on the World-wide-web and the limited access to a substantial part of them. This paper presents an investigation of the so-called word embeddings, a recent machine learning paradigm, with the aim of detecting plagiarism in French documents. The proposed model performs better than state of the art methods and achieves a plagiarism detection accuracy of 62% using Gutenberg project novels.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Plagdet is the main measure for plagiarism detection systems evaluation; it is especially dedicated to manually paraphrased plagiarism datasets.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Asghari, H., Fatemi, O., Mohtaj, S., Faili, H., Rosso, P.: On the use of word embedding for cross language plagiarism detection. Intell. Data Anal. 23, 661–680 (2019). https://doi.org/10.3233/IDA-183985
Belyy, A.V., Dubova, M.A.: Framework for Russian plagiarism detection using sentence embedding similarity and negative sampling. In: Proceedings of the International Conference Dialogue 2018, Computational Linguistics and Intellectual Technologies (2018)
Cox, W., Pincombe, B.: Cross-lingual latent semantic analysis. J. Aust. N.Z. Ind. Appl. Math. 48, C1054–C1074 (2008)
Hariharan, S.: Automatic plagiarism detection using similarity analysis. Int. Arab J. Inf. Technol. 9(4), 322–326 (2012)
Magooda, A., Mahgoub, A.Y., Rashwan, M., Fayek, M.B., Raafat, H.: RDI system for extrinsic plagiarism detection (RDI RED)—working notes for PANAraPlagDet at fire 2015. In: FIRE 2015 Working Notes Papers, Gandhinagar, India (2015)
Martin, B.: Plagiarism: a misplaced emphasis. J. Inf. Ethics 3(2), 36–47 (1994)
Menon, R.K.R., Fahad, A., Rajesh, N.R.: Representation learning for plagiarism detection. Int. J. Pure Appl. Math. 199(15), 1815–1822 (2018)
Oberreuter, G., L’Huilier, G., Rios, S., Velásquez, J.D.: Approaches for intrinsic and external plagiarism detection. In: Proceedings of the 3rd PAN@Conference and Labs of the Evaluation Forum (CLEF), Netherlands (2011)
Oberreuter, G., Velásquez, J.D.: Text mining applied to plagiarism detection: the use of words for detecting deviations in the writing style. Expert Syst. Appl. 40(9), 3756–3763 (2013)
Potthast, M., Stein, B., Barron-Cedeno, A., Rosso P.: An evaluation framework for plagiarism detection. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling), pp. 997–1005 (2010)
Rehurek, R., Sojka, P.: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010). http://radimrehurek.com/gensim/models/doc2vec.html
Sorg, P., Cimiano, P.: Cross-lingual information retrieval with explicit semantic analysis (2009)
Stamatatos, E.: Intrinsic plagiarism detection using character n-gram profilles (2009)
Stein, B., Lipka, N., Prettenhofer, P.: Intrinsic plagiarism analysis. Lang. Resour. Eval. 45(1), 63–82 (2011). https://doi.org/10.1007/s10579-010-9115y
Vani, K., Gupta, D.: Identifying document-level text plagiarism: a two phase approach. J. Eng. Sci. Technol. 12(12), 3226–3250 (2017)
Vani, K., Gupta, D.: Detection of idea plagiarism using syntax-semantic concept extractions with genetic algorithm. J. Expert Syst. Appl. 73, 11–26 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Elamine, M., Bougares, F., Mechti, S., Hadrich Belguith, L. (2021). Extrinsic Plagiarism Detection for French Language with Word Embeddings. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-49342-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49341-7
Online ISBN: 978-3-030-49342-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)