Future of Natural Language Processing (Human Like Conversation)

Abstract

Natural language processing (NLP) has revolutionized artificial intelligence by solving the challenge of enabling machines to understand, interpret, and reproduce human language. The importance of NLP lies in its applications in many areas, including medicine, education, business, and media. This study explores the basic concepts, methods, and developments in NLP and aims to provide a general introduction to the field. The study first outlines the history of NLP and traces its evolution from a policy-based approach to modern deep learning. Fundamental techniques such as tokenization, parsing, sentiment analysis, and language modeling are summarized to illustrate the diversity of available tools. Recent innovations, particularly Transformer-based architectures such as BERT and GPT, are important for demonstrating their performance in tasks such as translation, writing, and conversational AI. The paper concludes by highlighting the strengths and versatility of NLP and discussing current issues such as biases in language structure and the development of multilingualism. This summary provides valuable guidance for researchers and practitioners who wish to understand and contribute to the rapidly evolving nature of natural language processing.