Machine Learning based Plagiarism Detection for Marathi Language

Abstract

In today's modern environment, when data are easily accessible, plagiarism is the most pervasive problem. Hence, a system for identifying and controlling it is crucial. In a variety of languages, there are numerous approaches that may be used for the purpose, but they are insufficient for literature that is based in the Marathi language. Plagiarism detection is a critical aspect of maintaining academic integrity and ensuring the originality of content in various languages. The detection of plagiarism in languages with relatively less computational research, such as Marathi, presents unique challenges due to its complex linguistic structure, syntax, and morphology. This paper explores a machine learning-based approach for efficient plagiarism detection specifically tailored for the Marathi language. We introduced a machine learning-based plagiarism detection method in this research study. We utilised the learning techniques of naive bayes, svm and artificial neural networks. SVM research have shown an average accuracy of 90%, while Naive Bayes studies have shown an average accuracy of 71%. Studies employing a Neural Network for Marathi Language Plagiarism Detection reported an average accuracy of 95%. The results demonstrate that the proposed approach can effectively detect plagiarism in Marathi texts, offering a promising tool for researchers, educators, and content creators to uphold content authenticity and originality.