Published 18-04-2024
Keywords
- Deep Learning,
- Natural Language Processing,
- Sentiment Analysis,
- Machine Translation
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Deep Learning for Natural Language Processing (NLP) has revolutionized the way we interact with machines, enabling them to understand and generate human language with remarkable accuracy. This paper provides a comprehensive overview of deep learning techniques in NLP, focusing on two key tasks: sentiment analysis and machine translation. We discuss the evolution of deep learning in NLP, from early neural networks to advanced models like Transformers. We analyze the challenges faced in these tasks and explore how deep learning models address them. Additionally, we highlight recent advancements, open challenges, and future directions in deep learning for NLP.
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References
- Venigandla, Kamala, and Venkata Manoj Tatikonda. "Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies." Power System Technology 45.4 (2021).
- Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.
- Palle, Ranadeep Reddy. "The convergence and future scope of these three technologies (cloud computing, AI, and blockchain) in driving transformations and innovations within the FinTech industry." Journal of Artificial Intelligence and Machine Learning in Management 6.2 (2022): 43-50.
- Palle, Ranadeep Reddy. "Discuss the role of data analytics in extracting meaningful insights from social media data, influencing marketing strategies and user engagement." Journal of Artificial Intelligence and Machine Learning in Management 5.1
- (2021): 64-69.
- Palle, Ranadeep Reddy. "Compare and contrast various software development
- methodologies, such as Agile, Scrum, and DevOps, discussing their advantages, challenges, and best practices." Sage Science Review of Applied Machine Learning 3.2 (2020): 39-47
- Venigandla, Kamala. "Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare." Power System Technology 46.4 (2022).