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Natural Language Understanding with Python

You're reading from  Natural Language Understanding with Python

Product type Book
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Pages 326 pages
Edition 1st Edition
Languages
Author (1):
Deborah A. Dahl Deborah A. Dahl
Profile icon Deborah A. Dahl

Table of Contents (21) Chapters

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

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