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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

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613429
Length 326 pages
Edition 1st Edition
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Author (1):
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Deborah A. Dahl Deborah A. Dahl
Author Profile Icon Deborah A. Dahl
Deborah A. Dahl
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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

Statistical significance of differences

The last general topic we will cover in evaluation is the topic of determining whether the differences between the results of experiments we have done reflect a real difference between the experimental conditions, or whether they reflect differences that are due to chance. This is called statistical significance. Whether a difference in the values of the metrics represents a real difference between systems isn’t something that we can know for certain, but what we can know is how likely it is that a difference that we’re interested in is due to chance. Let’s suppose we have the situation with our data that’s shown in Figure 13.3:

Figure 13.3 – Two distributions of measurement values – do they reflect a real difference between the things they’re measuring?

Figure 13.3 – Two distributions of measurement values – do they reflect a real difference between the things they’re measuring?

Figure 13.3 shows two sets of measurements, one with a mean of 0, on the left, and one with a mean of 0.75, on the...

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