Natural Language Processing: Methods, Models & Applications

Natural Language Processing: Methods, Models & Applications offers a comprehensive overview of the techniques and technologies used to process and analyze human language computationally. The book begins with fundamental concepts such as text preprocessing, tokenization, sentence segmentation, morphological analysis, syntactic parsing, and semantic representation.
It then explores core NLP methods, including statistical language modeling, information retrieval, sequence labeling, topic modeling, and word embeddings. Modern approaches based on machine learning and deep learning—such as neural networks, recurrent and transformer-based models, contextual embeddings, and large language models—are introduced with conceptual clarity.
The book also highlights practical applications of NLP across domains, including sentiment analysis, machine translation, speech recognition, question answering, text summarization, information extraction, and conversational AI. Evaluation metrics, datasets, ethical considerations, bias in language models, and responsible AI practices are discussed to provide a balanced perspective.
Designed for undergraduate and postgraduate students, researchers, and industry professionals, this book combines theory with application. It serves as both a foundational textbook and a reference guide for understanding how NLP methods and models are developed and deployed to solve real-world language-related challenges.