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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

Why visualize?

Visualizing data means displaying data in a graphical format such as a chart or graph. This is almost always a useful precursor to training a natural language processing (NLP) system to perform a specific task because it is typically very difficult to see patterns in large amounts of text data. It is often much easier to see overall patterns in data visually. These patterns might be very helpful in making decisions about the most applicable text-processing techniques.

Visualization can also be useful in understanding the results of NLP analysis and deciding what the next steps might be. Because looking at the results of NLP analysis is not an initial exploratory step, we will postpone this topic until Chapter 13 and Chapter 14.

In order to explore visualization, in this chapter, we will be working with a dataset of text documents. The text documents will illustrate a binary classification problem, which will be described in the next section.

Text document dataset...

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