Transformer-Based Semantic Similarity Framework for Extrinsic Plagiarism Detection in Low-Resource Gujarati Language

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ID: 312676
2025
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Abstract
This research proposed a trans-based NLP model of plagiarism detection in Gujarati, a low-resource and morphologically rich language. The difficulties with Gujarati include the lineage of annotated corpora, complicated morphology and a variety of syntactic structures. A hybrid solution that incorporates both statistical and contextual embeddings is created in order to resolve these problems. The similarity score of 0.4214 generated by baseline TF -IDF and cosine similarity approaches demonstrated that they do not have high ability to capture semantic relations. An optimized BERT model scored much higher at 0.9935, as it shows better contextual comprehension and paraphrase recognition. The self-attention mechanism of the transformer is appropriate in predicting long-range dependencies, which allows identifying paraphrased and obfuscated text. The results highlight the usefulness of transformer-based representations in the low-resource language setting and provide a practical approach to enhancing plagiarism detection.
Reference Key
imported_1777055967_69ebb8dfe8c53 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gaurav Kumar Ameta
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
10.56979/1001/2025/1176
URL
https://jcbi.org/index.php/Main/article/view/1176
Keywords Keywords not found

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