Description
Natural Language Processing (NLP) is a core area of artificial intelligence that enables
machines to understand, interpret, and generate human language. With applications
ranging from search engines and machine translation to virtual assistants and
conversational systems, NLP has become an essential technology in modern
computing. This book is written to provide a clear and structured introduction to
NLP, combining linguistic foundations with statistical and neural approaches.
The primary objective of this book is to bridge theory and practice. While linguistic
concepts explain how language is structured and used, computational models
demonstrate how these structures can be learned and processed by machines. The
content is designed to balance conceptual clarity with practical relevance, allowing
readers to understand not only how NLP systems work, but also why particular
methods are used.
This book is intended for undergraduate and postgraduate students in Computer
Science, Artificial Intelligence, Data Science, and related fields, as well as researchers
and practitioners seeking a solid foundation in NLP. No prior background in
linguistics is assumed, and mathematical concepts are introduced progressively to
support learners from diverse academic backgrounds.
The chapters are organized to move from fundamentals to advanced topics. Early
sections introduce language structure, probability, and statistical models, followed
by vector semantics, sequence labeling, syntactic parsing, computational semantics,
discourse analysis, and speech processing. Modern neural architectures and
transformer-based models are included to reflect current developments in the field.
