Abstract
The integration of Cognitive Computing and Natural Language Processing (NLP) represents a revolutionary development of Artificial Intelligence, allowing the creation of systems capable of learning, reasoning, and communicating with people in a natural and meaningful way. This article explores the convergence of these technologies and highlights how they combine to form intelligent systems capable of understanding and interpreting human language. A comprehensive taxonomy of Cognitive Computing technologies in NLP is presented, which classifies key tools and techniques that improve machine understanding and language generation. The article also explores practical applications, in particular, to improve accessibility for people with visual impairments using advanced Artificial Intelligence-based tools, as well as to analyze political discourse on social networks, where these technologies provide insight into public sentiment and information dynamics. Despite significant achievements, several challenges persist. Ethical concerns, including biases in AI, data privacy and societal impact, are critical to address for responsible deployment. Language complexity poses interpretative challenges, while biases in multimodal data and real-world deployment difficulties impact model performance and scalability. Future directions are proposed to overcome these challenges through improved robustness, generalization, and explainability in models, as well as enhanced data privacy and scalable, resource-efficient deployment. This article thus provides a comprehensive view of current advancements and outlines a roadmap for a responsible and inclusive future of Cognitive Computing and NLP.