Natural Language Processing: A Literature Survey of Approaches, Applications, Current Trends, and Future Directions

This comprehensive survey explores the diverse and rapidly evolving landscape of Natural Language Processing (NLP), covering foundational approaches and wide-ranging applications across various industries. NLP, which lies at the intersection of computer science, artificial intelligence, and linguistics, aims to enable machines to understand, interpret, and generate human language. The paper delves into methodologies such as rule-based systems, statistical models, neural networks, and hybrid techniques. The survey examines NLP’s pivotal roles in key sectors. In healthcare, it enhances patient care and medical research through data analysis. In finance, it aids in sentiment analysis, risk assessment, and fraud detection. Education benefits from automated grading and personalized learning. Detailed analyses highlight applications in customer service with chatbots, legal compliance through document automation, disaster risk mitigation via data processing, and social media analysis for sentiment tracking. The paper also identifies current challenges, such as handling ambiguous language and ensuring data privacy, and discusses emerging research trends like deep learning advancements and sophisticated language models. By illuminating these aspects, the survey underscores NLP’s transformative potential and provides valuable insights for further exploration in this dynamic field.