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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

Problems after deployment

After an NLU system is developed and put into place in an application, it still requires monitoring. Once the system has reached an acceptable level of performance and has been deployed, it can be tempting to leave it alone and assume that it doesn’t need any more attention, but this is not the case. At the very least, the deployed system will receive a continuous stream of new data that can be challenging to the existing system if it is different from the training data in some way. On the other hand, if it is not different, it can be used as new training data. Clearly, it is better to detect performance problems from internal testing than to learn about them from negative customer feedback.

At a high level, we can think of new performance problems as either being due to a change in the system itself, or due to a change in the deployment context.

Changes in system performance due to system changes should be detected by testing before the new system...

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