This paper reviews significant deep learning models and methods used in natural language processing (NLP) and provides a comprehensive overview of their evolution. It discusses various models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and recursive neural networks, along with their applications in NLP tasks like sentiment analysis, question answering, and dialogue systems. The paper also covers memory-augmenting strategies, attention mechanisms, and how unsupervised models, reinforcement learning, and deep generative models have been employed for language-related tasks. It highlights the importance of distributed representations, word embeddings, and contextualized word embeddings in capturing semantic and syntactic information. The paper discusses the limitations of traditional word embeddings and proposes solutions such as task-specific embeddings and contextualized embeddings. It also explores the use of CNNs for sentence modeling and their applications in various NLP tasks, as well as the role of RNNs in capturing sequential information and long-term dependencies. The paper concludes with a discussion on the future directions of deep learning in NLP, emphasizing the need for further research and development in this field.This paper reviews significant deep learning models and methods used in natural language processing (NLP) and provides a comprehensive overview of their evolution. It discusses various models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and recursive neural networks, along with their applications in NLP tasks like sentiment analysis, question answering, and dialogue systems. The paper also covers memory-augmenting strategies, attention mechanisms, and how unsupervised models, reinforcement learning, and deep generative models have been employed for language-related tasks. It highlights the importance of distributed representations, word embeddings, and contextualized word embeddings in capturing semantic and syntactic information. The paper discusses the limitations of traditional word embeddings and proposes solutions such as task-specific embeddings and contextualized embeddings. It also explores the use of CNNs for sentence modeling and their applications in various NLP tasks, as well as the role of RNNs in capturing sequential information and long-term dependencies. The paper concludes with a discussion on the future directions of deep learning in NLP, emphasizing the need for further research and development in this field.