Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Natural Language Understanding with Python

You're reading from   Natural Language Understanding with Python Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systems

Arrow left icon
Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Length 326 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Deborah A. Dahl Deborah A. Dahl
Author Profile Icon Deborah A. Dahl
Deborah A. Dahl
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Getting Started with Natural Language Understanding Technology
2. Chapter 1: Natural Language Understanding, Related Technologies, and Natural Language Applications FREE CHAPTER 3. Chapter 2: Identifying Practical Natural Language Understanding Problems 4. Part 2:Developing and Testing Natural Language Understanding Systems
5. Chapter 3: Approaches to Natural Language Understanding – Rule-Based Systems, Machine Learning, and Deep Learning 6. Chapter 4: Selecting Libraries and Tools for Natural Language Understanding 7. Chapter 5: Natural Language Data – Finding and Preparing Data 8. Chapter 6: Exploring and Visualizing Data 9. Chapter 7: Selecting Approaches and Representing Data 10. Chapter 8: Rule-Based Techniques 11. Chapter 9: Machine Learning Part 1 – Statistical Machine Learning 12. Chapter 10: Machine Learning Part 2 – Neural Networks and Deep Learning Techniques 13. Chapter 11: Machine Learning Part 3 – Transformers and Large Language Models 14. Chapter 12: Applying Unsupervised Learning Approaches 15. Chapter 13: How Well Does It Work? – Evaluation 16. Part 3: Systems in Action – Applying Natural Language Understanding at Scale
17. Chapter 14: What to Do If the System Isn’t Working 18. Chapter 15: Summary and Looking to the Future 19. Index 20. Other Books You May Enjoy

Figuring out that a system isn’t working

Figuring out whether a system isn’t working as well as it should be is important, both during initial development as well as during ongoing deployment. We’ll start by looking at poor performance during initial development.

Initial development

The primary techniques we will use to determine that our system isn’t working as well as we'd like are the evaluation techniques we learned about in Chapter 13. We will apply those in this chapter. We will also use confusion matrices to detect specific classes that don’t work as well as the other classes.

It is always a good idea to look at the dataset at the outset and check the balance of categories because unbalanced data is a common source of problems. Unbalanced data does not necessarily mean that there will be accuracy problems, but it’s valuable to understand our class balance at the beginning. That way, we will be prepared to address accuracy...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at AU $24.99/month. Cancel anytime