Publication Details
Abstract
Natural Language Processing (NLP), situated at the intersection of artificial intelligence and linguistics, has witnessed unprecedented growth in recent years. Advances in deep learning, large language models, and computational linguistics have transformed how machines understand and generate human language. This review explores the challenges and opportunities arising from this interaction. Linguistic diversity, ambiguity, and context sensitivity remain major obstacles in achieving human-like comprehension, while ethical issues such as bias, cultural representation, and misuse of AI systems present additional hurdles. On the other hand, opportunities emerge in areas such as cross-linguistic communication, intelligent tutoring systems, sentiment analysis, healthcare applications, and automated translation. By bridging theoretical linguistics with applied AI methodologies, future research can foster more robust, fair, and contextually aware NLP systems that advance both linguistic theory and real-world applications.