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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General considerations for developing visualizations

Stepping back a bit from the specific techniques we have reviewed so far, we will next discuss some general considerations about visualizations. Specifically, in the next sections, we will talk about what to measure, followed by how to represent these measurements and the relationships among the measurements. Because the most common visualizations are based on representing information in the XY plane in two dimensions, we will mainly focus on visualizations in this format, starting with selecting among measurements.

Selecting among measurements

Nearly all NLP begins with measuring some property or properties of texts we are analyzing. The goal of this section is to help you understand the different kinds of text measurements that are available in NLP projects.

So far, we’ve primarily focused on measurements involving words. Words are a natural property to measure because they are easy to count accurately –...

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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

General considerations for developing visualizations

Selecting among measurements

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

General considerations for developing visualizations

Selecting among measurements

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Authors (1)

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