Natural Language Understanding with Python

Build advanced Natural Language Understanding Systems by acquiring data and selecting appropriate technology.

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 (NLU) organizes and structures, language allowing computer systems to effectively process textual information for many different practical applications. Natural Language Understanding with Python will help you explore practical techniques that make use of NLU to build a wide variety of creative and useful applications.
Complete with step-by-step explanations of essential concepts and practical examples, this book begins by teaching you 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 be introduced to practical issues such as acquiring data, evaluating systems, and deploying NLU applications, along with their solutions. This book is a comprehensive guide that will help you explore the full spectrum of essential NLU techniques and resources.
By the end of this book, you will be familiar with the foundational concepts of NLU, deep learning, and large language models (LLMs). You will be well on your way to having the skills to independently apply NLU technology in your own academic and practical applications.
What you will learn
The most important skill that readers will acquire is not just HOW to apply natural language techniques, but WHY to select particular techniques.
The book will also cover important practical considerations concerning acquiring real data and evaluating real system performance, not just performing textbook evaluations with pre-existing corpora.
After reading this book and studying the code, readers will be equipped to build state of the art as well as practical natural language applications to solve real problems.
How to develop and fine-tune an NLP application
Maintaining NLP applications after deployment
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
Natural Language Understanding, Related Technologies, and Natural Language Applications
Identifying Practical Natural Language Understanding Problems
Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning
Selecting Libraries and Tools for Natural Language Understanding
Natural Language Data – Finding and Preparing Data
Exploring and Visualizing Data
Selecting Approaches and Representing Data
Rule-Based Techniques
Machine Learning Part 1 - Statistical Machine Learning
Machine Learning Part 2 – Neural Networks and Deep Learning Techniques
Machine Learning Part 3 – Transformers and Large Language Models
Applying Unsupervised Learning Approaches
How Well Does It Work? – Evaluation
What to Do If the System Isn’t Working
Summary and Looking to the Future