Enhancing the accuracy of Contextual Word Sense Disambiguation in  Natural Language Processing Using a Transformer-Based Deep  Learning Model

Mrs. ROOPA H R,Dr. PANNEER AROKIARAJ S,Dr. MEENATCHI SUNDARAM

Enhancing the accuracy of Contextual Word Sense Disambiguation in Natural Language Processing Using a Transformer-Based Deep Learning Model

Authors

Mrs. ROOPA H R,Dr. PANNEER AROKIARAJ S,Dr. MEENATCHI SUNDARAM

Keywords:

AI in Linguistics, BERT, Contextual Embeddings, Deep Learning, Transformer Based WSD, Natural Language Processing, Semantic Understanding, Text Disambiguation, Machine Translation, Information Retrieval,

Abstract

The enhancement of Word Sense Disambiguation (WSD) capabilities gets research throughdeep learning models that use transformers BERT, ROBERTa and T5. Contextual embeddingsform the basis of these models which enhance the accuracy levels of Natural Language

References

Bevilacqua, M., & Navigli, R. (2020). Breaking through the 80% glass ceiling: Raising the state of the art in Word Sense Disambiguation by incorporating knowledge graph information. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2854–2864. https://doi.org/10.18653/v1/2020.aclmain.255

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Published

2024-08-20

How to Cite

Mrs. ROOPA H R,Dr. PANNEER AROKIARAJ S,Dr. MEENATCHI SUNDARAM. (2024). Enhancing the accuracy of Contextual Word Sense Disambiguation in Natural Language Processing Using a Transformer-Based Deep Learning Model . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 5611–5631. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3281