Enhancing Plagiarism Detection in Marathi with a Weighted Ensemble of TF-IDF and BERT Embeddings for Low-Resource Language Processing

2025-01-09

AI Summary

To address the low accuracy of plagiarism detection in the low-resource language Marathi, this paper proposes a weighted ensemble method integrating statistical and semantic features. Specifically, it is the first to combine multilingual BERT sentence embeddings—fine-tuned to capture deep semantic representations—with TF-IDF–based lexical features, and jointly leverages SVM and Random Forest classifiers. A dynamic weighted voting mechanism is further designed to balance lexical, syntactic, and semantic signals. Evaluated on a newly constructed Marathi plagiarism dataset, the system achieves an F1-score of 0.89—outperforming the best single model by 7.2% and significantly surpassing conventional approaches. This work empirically validates the effectiveness of semantic–statistical fusion for plagiarism detection in low-resource languages and establishes a reusable technical framework for NLP applications targeting under-resourced languages.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Ensemble Methods
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 under-explored, 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. This approach effectively captures statistical, semantic, and syntactic aspects of text features through a weighted voting ensemble of machine learning models.

Problem

• Plagiarism Detection
• Marathi Language
• BERT Effectiveness

Innovation

• TF-IDF_BERT_integration
• Marathi_plagiarism_detection
• multimodal_machine_learning_models