Machine Learning Applications in NLP: An In-Depth Review of Techniques  and Trends

Mrs. Ashu Nayak ,Ms. Aakansha Soy

Machine Learning Applications in NLP: An In-Depth Review of Techniques and Trends

Authors

Mrs. Ashu Nayak ,Ms. Aakansha Soy

Keywords:

Natural Language Processing, Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Deep Learning, Hybrid Models, Future Trends, Ethical AI

Abstract

This study provides an in-depth analysis of machine learning (ML) methodologies applied withinnatural language processing (NLP), encompassing supervised, unsupervised, reinforcement, deeplearning, and hybrid models. It begins with foundational overviews of each technique andexplores their relevance in solving various NLP tasks

References

Mikolov, T., et al. (2013). Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 3111-3119).

Manning, C. D., Raghavan, P., & Schütze, H. (2020). Introduction to Information Retrieval. Cambridge University Press.

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Published

2024-02-07

How to Cite

Mrs. Ashu Nayak ,Ms. Aakansha Soy. (2024). Machine Learning Applications in NLP: An In-Depth Review of Techniques and Trends. Journal of Computational Analysis and Applications (JoCAAA), 33(2), 1423–1431. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2556