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Natural Language Processing (NLP) is a pillar of modern artificial intelligence, allowing machines to understand, interpret, and generate human language. This paper is a thorough examination of the complex landscape of NLP, delving into its fundamental concepts, methodologies, and real-world applications. It defines the fundamental principles that underpin NLP, elucidating key techniques such as tokenization, syntactic and semantic analysis, and named entity recognition. Furthermore, it explains the evolution of NLP models, from rule-based systems to sophisticated machine learning architectures such as recurrent neural networks (RNNs), transformers such as BERT and GPT, and their impact on a variety of domains such as sentiment analysis, machine translation, chatbots, and more. The discussion goes on to discuss the challenges that NLP faces, ethical considerations, recent advancements such as transfer learning, and future trajectories. This paper synthesizes critical insights and analyses from recent research, fostering a comprehensive understanding of the significance and potential of NLP.
Gupta et al. (Fri,) studied this question.