Lee, Raymond S. T.
Natural Language Processing A Textbook with Python Implementation / [electronic resource] : by Raymond S. T. Lee. - 1st ed. 2024. - XXXII, 437 p. 1 illus. online resource.
Part I - Concepts and Technology -- Chapter 1. Introduction to Natural Language Processing -- Chapter 2. N-gram Language Model -- Chapter 3. Part-of-Speech Tagging -- Chapter 4. Syntax and Parsing -- Chapter 5. Meaning Representation -- Chapter 6. Semantic Analysis -- Chapter 7. Pragmatic Analysis and Discourse -- Chapter 8. Transfer Learning and Transformer Technology -- Chapter 9. Major Natural Language Processing Applications -- Part II -Natural Language Processing Workshops with Python Implementation in 14 Hours -- Chapter 10. Workshop#1 - Basics of Natural Language Toolkit (Hour 1-2) -- Chapter 11. Workshop#2 - N-grams Modeling with Natural Language Toolkit (Hour 3-4) -- Chapter 12. Workshop#3 - Part-of-Speech Tagging using Natural Language Toolkit (Hour 5-6) -- Chapter 13. Workshop#4 - Semantic Analysis and Word Vectors using spaCy (Hour 7-8) -- Chapter 14. Workshop#5 - Sentiment Analysis and Text Classification (Hour 9-10) -- Chapter 15. Workshop#6 - Transformers with spaCy and TensorFlow (Hour11-12) -- Chapter 16. Workshop#7 - Building Chatbot with TensorFlow and Transformer Technology (Hour 13-14).
This textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.
9789819919994
10.1007/978-981-99-1999-4 doi
Natural language processing (Computer science).
Artificial intelligence.
Computational intelligence.
Machine learning.
Python (Computer program language).
Artificial intelligence--Data processing.
Natural Language Processing (NLP).
Artificial Intelligence.
Computational Intelligence.
Machine Learning.
Python.
Data Science.
QA76.9.N38
006.35
Natural Language Processing A Textbook with Python Implementation / [electronic resource] : by Raymond S. T. Lee. - 1st ed. 2024. - XXXII, 437 p. 1 illus. online resource.
Part I - Concepts and Technology -- Chapter 1. Introduction to Natural Language Processing -- Chapter 2. N-gram Language Model -- Chapter 3. Part-of-Speech Tagging -- Chapter 4. Syntax and Parsing -- Chapter 5. Meaning Representation -- Chapter 6. Semantic Analysis -- Chapter 7. Pragmatic Analysis and Discourse -- Chapter 8. Transfer Learning and Transformer Technology -- Chapter 9. Major Natural Language Processing Applications -- Part II -Natural Language Processing Workshops with Python Implementation in 14 Hours -- Chapter 10. Workshop#1 - Basics of Natural Language Toolkit (Hour 1-2) -- Chapter 11. Workshop#2 - N-grams Modeling with Natural Language Toolkit (Hour 3-4) -- Chapter 12. Workshop#3 - Part-of-Speech Tagging using Natural Language Toolkit (Hour 5-6) -- Chapter 13. Workshop#4 - Semantic Analysis and Word Vectors using spaCy (Hour 7-8) -- Chapter 14. Workshop#5 - Sentiment Analysis and Text Classification (Hour 9-10) -- Chapter 15. Workshop#6 - Transformers with spaCy and TensorFlow (Hour11-12) -- Chapter 16. Workshop#7 - Building Chatbot with TensorFlow and Transformer Technology (Hour 13-14).
This textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.
9789819919994
10.1007/978-981-99-1999-4 doi
Natural language processing (Computer science).
Artificial intelligence.
Computational intelligence.
Machine learning.
Python (Computer program language).
Artificial intelligence--Data processing.
Natural Language Processing (NLP).
Artificial Intelligence.
Computational Intelligence.
Machine Learning.
Python.
Data Science.
QA76.9.N38
006.35