ADVANCES IN COMPUTATIONAL LINGUISTICS DRIVEN BY ARTIFICIAL INTELLIGENCE

Abstract

Artificial Intelligence (AI) has significantly transformed the field of computational linguistics by enabling more efficient and accurate processing of human language. This article explores the integration of AI techniques, particularly machine learning and deep learning, into language analysis and generation tasks. Traditional rule-based approaches are contrasted with modern data-driven models, highlighting improvements in performance and adaptability. Key technologies such as neural networks and transformer architectures are discussed in relation to their role in advancing natural language processing (NLP) applications, including machine translation, speech recognition, and sentiment analysis. Furthermore, the article examines current challenges, such as data bias, computational complexity, and ethical considerations in AI-driven language systems.