Аннотации:
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 di culties 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-e cient 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.
