Deep Learning for Natural Language Processing

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.