An In-Depth Review of Natural Language Processing (NLP): Progress, Challenges, and Future Directions

Abstract

Natural Language Processing (NLP) has emerged as a pivotal domain in artificial intelligence, enabling machines to understand, interpret, and generate human language. With the advent of deep learning techniques, especially transformer models like GPT and BERT, NLP has seen remarkable advancements in tasks such as language translation, sentiment analysis, and conversational agents. This review explores the key progressions in NLP, examining the impact of deep learning, transfer learning, and pre-trained models on the field. It also addresses the ongoing challenges, including language ambiguity, biases in models, and the limitations posed by resource constraints for low-resource languages. Furthermore, it highlights future directions in NLP, including multimodal integration, personalized systems, and the ethical considerations necessary to ensure fair and transparent use of these technologies. Despite the impressive achievements, the field continues to evolve, with substantial room for growth in achieving truly intuitive, accurate, and universally accessible NLP systems.