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Natural Language Processing is a comprehensive textbook designed for students, researchers, and
professionals aiming to understand and apply NLP techniques eectively. This book provides a
structured approach, balancing theoretical knowledge with practical applications, ensuring that readers gain both conceptual clarity and hands-on experience.
Salient Features:
Explores the history, evolution, and signicance of NLP, from symbolic AI to deep learning-based approaches.
Addresses challenges in processing Indian languages, code-mixed text, and low-resource language models, making the book relevant for multilingual AI systems.
Covers morphological, syntactic, and semantic processing, including traditional rule-based
techniques, statistical models, and deep learning-based word embeddings.
Discusses NLP’s role in chatbots, machine translation, sentiment analysis, information retrieval, and social media monitoring, along with ethical concerns like bias, transparency, and data privacy.
Introduces Python-based tools such as NLTK, spaCy, and Hugging Face Transformers, guiding
readers in implementing tokenization, parsing, and embedding generation for NLP tasks.
Features practical projects, including sentiment analysis, chatbot development, and speech-to-text pipelines, ensuring learners can apply concepts to real-world NLP challenges.
Includes Chapter-end review questions and objective type questions to enhance learning.
Natural Language Processing is a comprehensive textbook designed for students, researchers, and
professionals aiming to understand and apply NLP techniques eectively. This book provides a
structured approach, balancing theoretical knowledge with practical applications, ensuring that readers gain both conceptual clarity and hands-on experience.
Salient Features:
Explores the history, evolution, and signicance of NLP, from symbolic AI to deep learning-based approaches.
Addresses challenges in processing Indian languages, code-mixed text, and low-resource language models, making the book relevant for multilingual AI systems.
Covers morphological, syntactic, and semantic processing, including traditional rule-based
techniques, statistical models, and deep learning-based word embeddings.
Discusses NLP’s role in chatbots, machine translation, sentiment analysis, information retrieval, and social media monitoring, along with ethical concerns like bias, transparency, and data privacy.
Introduces Python-based tools such as NLTK, spaCy, and Hugging Face Transformers, guiding
readers in implementing tokenization, parsing, and embedding generation for NLP tasks.
Features practical projects, including sentiment analysis, chatbot development, and speech-to-text pipelines, ensuring learners can apply concepts to real-world NLP challenges.
Includes Chapter-end review questions and objective type questions to enhance learning.
Dr. C. Muthu is currently Head, Department of Data Science, Loyola College, Chennai, Tamil Nadu. An
experienced computer professional of over 38 years. Dr. C. Muthu has been teaching Python, Machine
Learning for 9 years. A prolic writer, his books include Programming with Java, Visual C#. Net and Basic.Net.
Dr. T. Rajaretnam is currently Head, Department of Data Science, St. Joseph's College, Trichy, Tamil Nadu.An experienced computer professional with over 36 years of experience, Dr. T. Rajaretnam has been teaching Python, Machine Learning, and Compiler Design for several years. He is also a passionate researcher and has published numerous research papers in reputed journals.
Mr. M. C. Prakash is currently providing consultancy services for Data Science projects at Shalom Infotech. He is an alumnus of elite institutions such as CEG and BIM. An IT Professional with 7 years of work experience in well known MNCs such as IBM and Cognizant, he is also a passionate researcher who has published five research papers in Analytics domain.