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

View More author details

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

Why subscribe?

20. Other Books You May Enjoy

Future directions in NLU technology and research

While the recent improvements in NLU technology based on transformers and LLMs, which we reviewed in Chapter 11, have resulted in very impressive capabilities, it is important to point out that there are many topics in NLU that are far from solved. In this section, we will look at some of the most active research areas – extending NLU to new languages, speech-to-speech translation, multimodal interaction, and avoiding bias.

Quickly extending NLU technologies to new languages

A precise count of the number of currently spoken languages is difficult to obtain. However, according to WorldData.info, there are currently about 6,500 languages spoken throughout the world (https://www.worlddata.info/languages/index.php#:~:text=There%20are%20currently%20around%206%2C500,of%20Asia%2C%20Australia%20and%20Oceania). Some languages, such as Mandarin, English, Spanish, and Hindi, are spoken by many millions of people, while other languages...

The rest of the chapter is locked

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

Future directions in NLU technology and research

Quickly extending NLU technologies to new languages

Authors (1)

Personalised recommendations for you

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

Future directions in NLU technology and research

Quickly extending NLU technologies to new languages

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you