Deep Learning Techniques for Natural Language Processing: A Comprehensive Review

Abstract

Natural Language Processing (NLP) has experienced rapid advancements with the emergence of deep learning techniques, enabling machines to understand, interpret, and generate human language with unprecedented accuracy. Traditional rule-based and statistical approaches often struggled with feature engineering, contextual ambiguity, and scalability, limiting their effectiveness in real-world applications. This paper presents a comprehensive review of deep learning techniques employed in NLP, systematically tracing their evolution from early neural language models and distributed word representations to advanced sequence-based architectures and state-of-the-art Transformer models. The study critically examines key deep learning architectures, including Recurrent Neural Networks, Long Short-Term Memory networks, Convolutional Neural Networks, attention mechanisms, and pre-trained language models such as BERT, GPT, and their variants. In addition, the review analyzes benchmark datasets, evaluation metrics, and major application domains, while highlighting existing challenges related to computational complexity, interpretability, data bias, and low-resource language processing. By synthesizing recent research findings and identifying emerging trends, this paper provides valuable insights into the current state and future directions of deep learning-driven NLP systems, serving as a foundational reference for researchers and practitioners alike.