Applications of Natural Language Processing: A Comprehensive Study

Abstract

Abstract--Natural Language Processing (NLP) has emerged as a major branch of Artificial Intelligence (AI) that allows computers to effectively understand, interpret, and generate human language. The recent advances in machine learning, deep learning and transformer-based architectures have considerably improved the performance of NLP systems on a wide range of applications. This paper presents a comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT. It covers the major methodologies including text preprocessing, feature representation, machine learning, deep learning and transformer-based language modelling. Moreover, the study elaborates on the use of NLP in healthcare, education, business, finance, customer service, social media, and intelligent communication and highlights its role in enhancing automation, decision-making, and human–computer interaction. In addition, the paper discusses the major challenges faced by current NLP systems, including language ambiguity, multilingual processing, computational complexity, model bias, privacy, and explainability. Finally, future research directions, including lightweight language models, multilingual NLP, explainable AI, and multimodal intelligence, are presented. The findings demonstrate that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.