Understanding Natural Language Processing strives to elucidate the fundamental principles of natural language processing (NLP) from various angles, encompassing conceptual, mathematical, and algorithmic perspectives, along with practical insights into tools and software usage.
The primary emphasis, however, is directed towards prompting readers to contemplate applications in the realm of natural language processing, enabling them to seamlessly integrate NLP components into real-time applications. The concepts are explored through the lens of machine learning and deep learning methodologies, accompanied by relevant use cases threaded throughout the chapters.
Features –
1. Discusses comparison of language models, basic vector models and neural language models
2. Introduces basic concepts of word, morphology and semantics
3. Explains word embedding and deep learning models including pre-trained models
4. Emphasizes on machine learning and deep learning approaches to NLP tasks – part-of speech tagging, syntactic processing, semantic processing and discourse and dialog systems including the latest ChatGPT architecture
5. Illustrates NLP from an application perspective – machine learning and deep learning approaches to text categorization, machine translation, information extraction, question answering and summarization
6. Covers ethics of NLP including bias and fairness
7. Rich pedagogy – objective-type questions, activities, case-studies and project-based learning exercises.
About the Author -
T V Geetha is a retired senior professor of Computer Science and Engineering with over 40 years of teaching experience in the areas of artificial intelligence, natural language processing, machine learning, deep learning and information retrieval. She has been instrumental in the formulation and updating of NLP syllabus in many universities. In addition, the Department of Information technology, Government of India requested her group to offer a specialized programme - M.E. CSE (with Specialization in Knowledge Engineering and Computational Linguistics (NLP)). The highlights included the framing of regulations. Syllabus and curriculum with state-of–the-art topics in Knowledge Engineering and NLP including Statistical NLP, Indian Language Processing, Applications of NLP, etc.
Her research interests include semantic, personalized and deep web search, semi-supervised learning for Indian language processing, application of Indian philosophy to knowledge representation and reasoning, machine learning for adaptive learning and application of machine learning and deep learning to biological literature mining and drug discovery. Her focused research in NLP included morphological processing, named entity recognition, relation extraction, summarization, semantic processing, lyric mining, natural language-based search which resulted in an innovative semi-supervised methodology for NLP specially for Indian language processing. She has co-authored a book titled “Machine Learning- Concepts, Techniques and Applications”.
She is a recipient of the Young Women Scientist Award from the government of Tamilnadu and Women of Excellence Award from the Rotaract Club of Chennai. For her pioneering work in NLP she was awarded the BSR Faculty Fellowship for Superannuated Faculty from the University Grants Commission, Government of India for 2020-2023. Currently she is the Distinguished Research Professor, at Sri Sivasubramanian Nadar College, Chennai and Curriculum Advisor at Rajalakshmi Engineering College, Chennai.
Book Contents –
1. Introduction 2. Approaches to Natural Language Processing 3. Text Classification 4. Language Modelling 5. Words, Morphology and Semantics 6. Representation of Text – Basic Vector Models 7. Neural Language Models - I 8. Vector Models – Word Embedding 9. Transformers and Pre-trained Models 10. POS Tagging and Sequence Labelling 11. Syntactic Processing 12. Semantic Processing 13. Discourse, Dialogue and ChatGPT 14. Applications of NLP 15. Ethical Aspects of NLP
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