Understanding of NLP to Machine Learning Language

Author(s)	Shreya Sanjay Rane, Avishkar Vilas Mandlik, Pratiksha Sawant
Country	India
Abstract	The research paper discusses advancements in Natural Language Processing (NLP), emphasizing machine learning and deep learning techniques. It covers key models such as Recurrent Neural Networks (RNNs), Transformers, BERT, and Universal Transformers. The paper explores challenges like contextual understanding, multilingual support, and bias in NLP systems. Additionally, it highlights methodologies like attention mechanisms, hierarchical attention networks, and universal transformers. It also discusses real-world applications in healthcare, finance, education, and customer service. Finally, it identifies future research directions to improve NLP's efficiency, scalability, and ethical considerations.
Keywords	Natural Language Processing (NLP), Deep Learning, Transformers, BERT (Bidirectional Encoder Representations from Transformers), Attention Mechanisms, Contextual Understanding, Multilingual Support, Bias and Fairness, Recurrent Neural Networks (RNNs), Word Embeddings
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-17
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.38953
Short DOI	https://doi.org/g89364

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