Authors: Prof. Ganesh Namdeo Dhengle, Dr. Bhavana B. Nerkar, Prof. Sandeep Ramesh Gadekar, and Dr. Preeti Pandurang Kale

ISBN: 978-81-69297-53-0

DOI: https://doi.org/10.59646/664

Date of Publication: April 24, 2026

Cite this book: Ganesh ND, Bhavana BN, Sandeep RG, and Preeti PK, (2026), Natural Language Processing, San International Scientific Publications, ISBN: 978-81-69297-53-0, DOI: https://doi.org/10.59646/664

Preface

Natural Language Processing is conceived as a comprehensive, in-depth, and systematically structured guide to understanding how machines interact with human language, combining linguistic theory, computational techniques, and modern artificial intelligence into a unified learning framework. The book begins by tracing the evolution of NLP from early rule-based systems to statistical approaches and, ultimately, to the deep learning revolution that has redefined the field. It establishes a strong linguistic foundation by exploring key components such as morphology, syntax, semantics, and pragmatics, enabling readers to appreciate how language structure and meaning are modeled computationally. Building on this base, the text carefully walks through the complete NLP pipeline—from text acquisition and preprocessing to tokenization, normalization, stemming, lemmatization, and sentence segmentation—followed by essential syntactic analysis techniques including part-of-speech tagging, chunking, and both dependency and constituency parsing. It further introduces statistical NLP concepts such as n-gram models, smoothing techniques, and probabilistic language modeling, before advancing into distributional semantics through word embeddings like Word2Vec and GloVe, which capture contextual meaning in vector space representations. The book then transitions into machine learning and deep learning paradigms, covering supervised and unsupervised methods, sequence models such as RNNs, LSTMs, and GRUs, and the transformative impact of attention mechanisms and encoder–decoder architectures.

A significant emphasis is placed on transformer-based models, transfer learning, and contextual embeddings such as BERT, GPT, and related architectures, highlighting their role in handling long-range dependencies and enabling state-of-the-art performance across NLP tasks. Moving beyond foundational techniques, the book delves into higher-level language understanding, including word sense disambiguation, named entity recognition, semantic role labeling, discourse analysis, and commonsense reasoning, supported by knowledge graphs and semantic networks. It also provides extensive coverage of practical applications such as text classification, sentiment analysis, topic modeling, and both extractive and abstractive summarization, along with evaluation metrics like BLEU, ROUGE, METEOR, and F1-score. Expanding the scope, the text explores multilingual processing, neural machine translation, speech recognition, text-to-speech systems, and multimodal learning that integrates text, vision, and audio into unified models. The book further examines conversational AI, including dialogue systems, chatbots, and question answering frameworks, with attention to real-world deployment and optimization strategies. Importantly, it addresses critical issues of evaluation, ethics, bias, fairness, privacy, interpretability, and robustness, encouraging responsible development and deployment of NLP systems. Finally, it looks toward the future of the field by discussing large language models, prompt engineering, retrieval-augmented generation, neuro-symbolic approaches, efficient model design, and emerging trends in generative AI. By seamlessly integrating theoretical rigor, practical implementation, and cutting-edge advancements, this book aims to equip students, researchers, and practitioners with the knowledge and skills necessary to understand, build, and innovate in the rapidly evolving domain of natural language processing.