"Natural Language Understanding" explores how AI systems are increasingly able to interpret and respond to human language. It examines core principles like contextual modeling, where AI grasps meaning from surrounding text, and semantic interpretation, which extracts deeper meaning beyond simple keyword recognition.
The book traces the evolution of Natural Language Understanding (NLU) from rule-based systems to modern deep learning approaches, highlighting the revolutionary impact of transformer networks.
The book argues that recent advances, especially in transformer networks, signify a shift towards genuine comprehension rather than mere text processing.
The book presents a comprehensive overview of NLU, beginning with the basics of linguistics and machine learning, then moving into contextual modeling and transformer architectures. Case studies and empirical results are used to support the arguments.
The book progresses from fundamental concepts to advanced techniques and practical applications. It emphasizes real-world implementation, offering insights into how NLU can be applied across various industries.
By addressing ethical considerations and future directions, "Natural Language Understanding" provides a valuable resource for anyone seeking to understand how machines can truly comprehend human language.
