Natural Language Processing: Advances, Challenges, and Opportunities - Deepam Sharma; Priyanka (AG Volumes, 2025)
Natural Language Processing (NLP) has become a game changer in the field of Artificial Intelligence to the extent that machines can understand, interpret and produce human language. This review paper is a systematic discussion of current developments, ongoing issues, and prospective opportunities in NLP. It follows the development of rule-based and statistical approaches to deep learning and transformer-based systems, such as large language models, which can be used in sentiment analysis, machine translation, chatbots, speech recognition, and domain-specific systems in medical, finance, legal services, and public health. The work summarizes the recent research on the topic of multilingual processing, low-resource language, resistance to adversarial attack, and domain adaptation. Moreover, it analyzes such critical issues as ambiguity, bias, scalability, sparsity of data, and ethical issues. Comprising the modern research results, this review provides the future research directions, which will enhance the robustness, interpretability, inclusiveness, and sustainable deployment of NLP systems in various linguistic and industrial settings.