Natural Language Understanding with Python: Combine natural language technology, deep learning, and large language models to create human-like languag (Paperback or Softback)

Build advanced NLU systems by utilizing NLP libraries such as NLTK, SpaCy, BERT, and OpenAI; ML libraries like Keras, scikit-learn, pandas, TensorFlow, and NumPy, along with visualization libraries such as Matplotlib and Seaborn.

Purchase of the print Kindle book includes a free PDF eBook

Key Features

• Master NLU concepts from basic text processing to advanced deep learning techniques
• Explore practical NLU applications like chatbots, sentiment analysis, and language translation
• Gain a deeper understanding of large language models like ChatGPT

Book Description

Natural Language Understanding facilitates the organization and structuring of language allowing computer systems to effectively process textual information for various practical applications. Natural Language Understanding with Python will help you explore practical techniques for harnessing NLU to create diverse applications.

with step-by-step explanations of essential concepts and practical examples, you’ll begin by learning about NLU and its applications. You’ll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you’ll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you’ll also discover practical issues such as acquiring data, evaluating systems, and deploying NLU applications along with their solutions. The book is a comprehensive guide that’ll help you explore techniques and resources that can be used for different applications in the future.

By the end of this book, you’ll be well-versed with the concepts of natural language understanding, deep learning, and large language models (LLMs) for building various AI-based applications.

What you will learn

• Explore the uses and applications of different NLP techniques
• Understand practical data acquisition and system evaluation workflows
• Build cutting-edge and practical NLP applications to solve problems
• Master NLP development from selecting an application to deployment
• Optimize NLP application maintenance after deployment
• Build a strong foundation in neural networks and deep learning for NLU

Who this book is for

This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.

Table of Contents

1. Natural Language Understanding, Related Technologies, and Natural Language Applications
2. Identifying Practical Natural Language Understanding Problems
3. Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning
4. Selecting Libraries and Tools for Natural Language Understanding
5. Natural Language Data – Finding and Preparing Data
6. Exploring and Visualizing Data
7. Selecting Approaches and Representing Data
8. Rule-Based Techniques
9. Machine Learning Part 1 - Statistical Machine Learning
10. Machine Learning Part 2 – Neural Networks and Deep Learning Techniques
11. Machine Learning Part 3 – Transformers and Large Language Models
12. Applying Unsupervised Learning Approaches
13. How Well Does It Work? – Evaluation
14. What to Do If the System Isn't Working
15. Summary and Looking to the Future

Deborah A. Dahl is the principal at Conversational Technologies, with over 30 years of experience in natural language understanding technology. She has developed numerous natural language processing systems for research, commercial, and government applications, including a system for NASA, and speech and natural language components on Android. She has taught over 20 workshops on natural language processing, consulted on many natural language processing applications for her customers, and written over 75 technical papers. Th is is Deborah’s fourth book on natural language understanding topics. Deborah has a PhD in linguistics from the University of Minnesota and postdoctoral studies in cognitive science from the University of Pennsylvania.