Enhancing Plagiarism Detection in M arathi with a Weighted Ensemble of TF - IDF and BERT Embeddings for Low-Resource Language Processing (2025)

Abstract: Plagiarism involves using another person’s work or concepts without proper attribution, presenting them as original creations. With the growing amount of data communicated in regional languages such as Marathi—one of India’s regional languages—it is crucial to design robust plagiarism detection systems tailored for low-resource languages. Language models like Bidirectional Encoder Representations from Transformers (BERT) have demonstrated exceptional capability in text representation and feature extraction, making them essential tools for semantic analysis and plagiarism detection. However, the application of BERT for low-resource languages remains underexplored, particularly in the context of plagiarism detection. This paper presents a method to enhance the accuracy of plagiarism detection for Marathi texts using BERT sentence embeddings in conjunction with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation. By combining TF-IDF with BERT, the system’s performance is significantly improved, which is especially pronounced in languages where BERT models are not extremely robust due to a lack of resources and corpora. This approach effectively captures statistical, semantic, and syntactic aspects of text features through a weighted voting ensemble of machine learning models.

## introduction plagiarism is a pervasive issue across various industries. while extensive research has focused on detecting plagiarized texts in widely spoken languages like english, similar advancements for regional languages, particularly marathi -a language spoken in india -are lacking. language models for text representation, such as bert @xcite , which are often used for semantic-based plagiarism detection, are significantly more robust for these commonly spoken languages due to the abundance of training corpora. in contrast, the scarcity of resources for marathi leads to weaker semantic analysis, resulting in less accurate plagiarism detection. most existing approaches to plagiarism detection in marathi rely on techniques such as syntax, fuzzy matching, structural analysis, or stylometry @xcite , which often overlook the meaning of texts and the linguistic nuances involved. consequently, these methods can yield inaccurate results. this study aims to evaluate the efficiency of recently fine-tuned versions of bert @xcite for marathi in extrinsic plagiarism detection-a method that identifies plagiarism by comparing input documents with a reference database of texts. we propose a system that integrates bert embeddings with tf-idf @xcite vectors, advancing the research and development of hybrid plagiarism detection models that combine syntactic, semantic, and statistical features of low-resource languages to achieve more accurate classifications. the contributions foot_0 of this paper are as follows: the previous work on paraphrase and plagiarism detection in marathi has not fully explored the efficiency of bert for semantic-based extrinsic plagiarism detection. bert embeddings are superior in capturing semantic relationships, offering context-sensitive, dense vector representations of words through deep learning @xcite . @xcite introduced mahasbert-sts, a specialized variant of the sbert (sentence-bert) model @xcite trained on natural language inference (nli) and semantic textual similarity (sts) datasets, making it wellsuited for accurately capturing semantic similarity in marathi texts and identifying plagiarism. research on plagiarism and paraphrase detection has expanded to other indian languages. for instance, @xcite and @xcite em-ployed similarity measures, including cosine similarity, jaccard similarity, edit distance, and dice distance, to train gradient boosting tree @xcite and probabilistic neural network (specht, 1990) classification models, respectively, for identifying paraphrased texts in hindi, punjabi, malayalam, and tamil. in a similar approach, @xcite and @xcite computed normalized idf scores and word overlap, demonstrating the high performance of random forest classifiers in their analyses. additionally, sarkar (2016b) utilized cosine similarity through tf-idf vectorization, alongside word overlap and semantic similarity via word2vec @xcite , to train a multinomial logistic regression model aimed at identifying paraphrasing in indian languages. furthermore, @xcite proposed deep learning models based on convolutional neural networks and recurrent neural networks for paraphrase detection in both hindi and english, assessing the effectiveness of wordnet and word2vec embeddings for feature extraction. previous works largely used precomputed similarity scores as input features to classification models, with many of these scores lacking semantic depth, which limited the models to learning from the scores rather than from the text itself. additionally, word2vec embeddings, while useful, provide static representations of words, overlooking context-a limitation addressed by bert embeddings, which adapt to the context of each word. studies in plagiarism and paraphrase detection have shown that combining statistical features, such as tf-idf vectorization, with semantic features from deep learning models enhances detection performance. for instance, arabi and akbari (2022) integrated semantic features from word-net and fasttext @xcite with tf-idf weighting for effective plagiarism detection. similarly, @xcite combined cnn-lstm @xcite and wordnet-based semantic features with statistical measures like tf-idf similarity and n-gram overlap to improve paraphrase detection. these studies underscore the potential of hybrid approaches, especially for low-resource languages, where features extracted from fine-tuned bert models alone may not yield optimal results. ## methodology instead of relying on precomputed similarity scores and overlaps, our approach involves feeding the model with direct numeric representations of the text, enabling it to learn from the inherent patterns in the language rather than abstracted metrics. the following sections cover our data collection and preprocessing procedures, the method for text representation and feature extraction, the proposed system architecture and implementation details. ## results and discussion we evaluated and fine-tuned various classification models, including our proposed weighted ensemble system, to achieve optimal performance. ## conclusion we proposed a weighted ensemble voting system that leverages both tf-idf and bert-based text representations to detect extrinsic plagiarism and paraphrasing in marathi text. our system not only outperformed individual classification models but also demonstrated the complementary value of using tf-idf vectors alongside bert embeddings, resulting in enhanced classification accuracy over bert-only and tf-idf-only models. by exploring various model combinations, weight configurations, and embedding dimensions, we identified an optimal configuration that achieved a remarkable accuracy of 82.04% using bert embeddings of size 768 from mahasbert-sts alongside tf-idf vectors of size 400, thereby surpassing the performance of other classification models. this study highlights the effectiveness of combining statistical text vectorization methods, such as tf-idf, with context-based embeddings like bert to capture both statistical and semantic as-