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

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781804613429

Length 326 pages

Edition 1st Edition

Languages

Python

Tools

Combine

Concepts

Deep Learning

Author (1):

Deborah A. Dahl

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Table of Contents (21) Chapters

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

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Exploring and Visualizing Data

Exploring and visualizing data are essential steps in the process of developing a natural language understanding (NLU) application. In this chapter, we will explore techniques for data exploration, such as visualizing word frequencies, and techniques for visualizing document similarity. We will also introduce several important visualization tools, such as Matplotlib, Seaborn, and WordCloud, that enable us to graphically represent data and identify patterns and relationships within our datasets. By combining these techniques, we can gain valuable perspectives into our data, make informed decisions about the next steps in our NLU processing, and ultimately, improve the accuracy and effectiveness of our analyses. Whether you’re a data scientist or a developer, data exploration and visualization are essential skills for extracting actionable insights from text data in preparation for further NLU processing.

In this chapter, we will cover several...

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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

Table of Contents (21) Chapters

Exploring and Visualizing Data

Authors (1)

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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

Table of Contents (21) Chapters

Exploring and Visualizing Data

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