Raymond M. Lee · 2025
Motivation of This BookNatural language processing (NLP) and its associated applications have flourished due to advancements in artificial intelligence (AI) over the past few decades.NLP applications include information retrieval (IR) systems, text summarization (TS) systems, question-and-answering (chatbot) systems, as well as recent developments in large language models (LLMs) and generative AI (GenAI).These topics are prevalent in both industry and academia, offering varied routines that significantly enhance a wide range of everyday services.The objective of this book is to provide readers with foundational NLP concepts and knowledge through 14 h of step-by-step workshops.These workshops will guide participants in practicing various core Python-based NLP tools, including NLTK, spaCy, TensorFlow, Keras, transformer, and BERT technology, to build their own Python-based NLP applications. Organization and Structure of This BookThis book is structured into two main parts: Part I-Concepts and Technology, and Part II-Natural Language Processing Workshops with Python Implementation in 14 Hours.In Part I, the first ten chapters lay a solid foundation in natural language processing (NLP) concepts, exploring key topics such as N-gram language models, part-of-speech tagging, syntax analysis, semantic representations, and latest advancements in transfer learning and transformer technology.Each chapter builds on the preceding one, leading to an understanding of major NLP applications and the evolution of large language models and generative AI, while also addressing ethical considerations in AI.Part II consists of seven practical workshops that provide hands-on experience with Python implementations relevant to the concepts discussed in the first part.viiiStarting with an introduction to the Natural Language Toolkit (NLTK), the workshops guide readers through various NLP tasks such as N-gram modeling, sentiment analysis, and use of transformers like BERT.Each workshop offers step-by-step instructions, empowering readers to apply their knowledge in real-world scenarios, including the creation and deployment of a chatbot system.This structured approach balances foundational theory with practical application, making it suitable for learners eager to advance their skills in NLP. Major Enhancement in 2nd EditionThe second edition includes the following major updates:1.An overview of the development of BERT, transformer models, ChatGPT, and large language models (LLMs) from the 2000s to the present, covered in Sect.1.5, "A Brief History of NLP," in Chap. 1 2. A new chapter focusing on the latest advancements in NLP, specifically in LLMs and generative AI (GenAI), included in Chap. 10 3. Revised and updated NLP workshops (originally Chaps.10, 11, 12, 13, 14, 15, and 16, now Chaps.11, 12, 13, 14, 15, 16, and 17) to align with the latest versions of Python-based NLP packages and tools Readers of This BookThis book serves as both an NLP textbook and a practical guide for NLP Python implementation, tailored for: Undergraduates and postgraduates across various disciplines, including AI, computer science, IT, data science, and related fields Lecturers and tutors teaching NLP or AI-related courses NLP and AI scientists, as well as developers, who wish to learn the fundamental concepts of NLP and apply them through Python-based workshops Readers interested in learning NLP concepts and practicing Python-based NLP techniques using tools such as NLTK, spaCy, TensorFlow, Keras, BERT, transformer technology, and latest developments in LLMs and GenAI Preface ix How to Use This Book?This book can be used as a textbook for undergraduate and postgraduate courses on natural language processing (NLP) and as a reference for general readers who want to learn key NLP technologies and implement NLP applications using contemporary tools such as NLTK, spaCy, TensorFlow, BERT, and transformer technology.Part I (Chaps.1, 2, 3, 4, 5, 6, 7, 8, 9, and 10) covers the foundational concepts and key technologies in NLP, including the N-gram language model, part-of-speech tagging, syntax and parsing, meaning representation, semantic analysis, pragmatic analysis, transfer learning, and transformer technology.It also discusses major NLP applications and the latest developments in BERT, ChatGPT, large language models (LLMs), and generative AI (GenAI).Part II (Chaps.11, 12, 13, 14, 15, 16, and 17) consists of materials for a 14-h, step-by-step Python-based NLP implementation spread across seven workshops.For readers and AI scientists, this book serves as a reference for learning NLP and applying Python-based NLP tools and libraries, using the latest development tools and platforms.For the seven NLP workshops in Part II (Chaps.11, 12, 13, 14, 15, 16, and 17), readers can download all Jupyter Notebook files and
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