From Syntax to Semantics: The Intersection of Natural Language Processing and Generative AI

Natural Language Processing (NLP) is a fast-expanding subject within artificial intelligence that focuses on enabling robots to understand, interpret, and synthesize human language. This research paper provides a detailed examination of NLP, covering its historical evolution, methodologies, applications, and its association with Generative AI (GenAI). The paper explores the transition from rule-based approaches to deep learning models such as transformers, highlighting their impact on modern NLP applications like sentiment analysis, machine translation, and chatbots. Furthermore, it discusses the role of NLP in technological advancements and its integration with big data and cloud computing. By addressing current challenges and future directions, this study contributes to a deeper understanding of NLP's potential and its expanding influence across various industries. Key Words: Natural Language Processing, Semantic analysis, Generative AI, Syntax Analysis, Natural Language Generation