The Evolution of Natural Language Processing and its Influence on Artificial Intelligence: A Review and Future Research Directions

Abstract

Introduction: This study reviews the significant developments in natural language processing (NLP) and its impact on artificial intelligence (AI), focusing on advancements in language models, computational infrastructures, and the integration of machine learning methods. Methodology: A systematic literature review was conducted using the PRISMA guidelines, targeting articles from 2022 to 2024. Web of Science with search terms like “natural language processing”, “PNL”. Results: The review highlights the critical role of advanced language models such as GPT-4, BERT, and their variants in improving natural language understanding and generation, the importance of high-performance computing infrastructures, and the use of machine learning techniques to optimize NLP tasks. Discussions: The findings confirm the relevance of robust computational infrastructures and reveal new perspectives on the rapid evolution and broader adoption of NLP techniques across various sectors. Conclusions: Continued investment in computational infrastructures and the development of advanced language models is essential. Future research should expand the study period, diversify languages, include grey literature, conduct longitudinal studies, and explore the ethical and privacy challenges in implementing NLP techniques.