@article {10.3844/jcssp.2025.2631.2646,
article_type = {journal},
title = {Few-shot Fine-tuning of BERT Multilingual for Hindi Word Sense Disambiguation },
author = {Patel, Shailendra Kumar and Kumar, Rakesh and Sirohi, Anuj Kumar},
volume = {21},
number = {11},
year = {2025},
month = {Dec},
pages = {2631-2646},
doi = {10.3844/jcssp.2025.2631.2646},
url = {https://thescipub.com/abstract/jcssp.2025.2631.2646},
abstract = {Word Sense Disambiguation (WSD) is a fundamental task in Natural Language Processing (NLP), addressing the challenge of identifying correct word meanings in context. This task is particularly complex for morphologically rich and resource-limited languages like Hindi, which exhibit significant lexical ambiguity compounded by limited availability of annotated corpora. To address these challenges, we propose a supervised approach combining the multilingual BERT model (mBERT) with Hindi WordNet as a structured lexical resource. Using few-shot learning, we fine-tune mBERT on a dataset constructed from Hindi WordNet to disambiguate contextually ambiguous words across four parts of speech (POS): nouns, verbs, adjectives, and adverbs. Experiments on standard Hindi WSD benchmarks demonstrate that our method significantly outperforms traditional rule-based and embedding-based approaches, achieving 96.48% accuracy—an approximate 3% improvement over the strongest baseline. These results validate the effectiveness of integrating contextualized embeddings from pre-trained language models with structured lexical databases, highlighting the promise of hybrid techniques for advancing WSD in low-resource languages and providing a framework applicable to other morphologically complex languages with similar resource constraints.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}