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

Rule-based approaches

The basic idea behind rule-based approaches is that language obeys rules about how words are related to their meanings. For example, when we learn foreign languages, we typically learn specific rules about what words mean, how they’re ordered in sentences, and how prefixes and suffixes change the meanings of words. The rule-based approach to NLU operates on the premise that these kinds of rules can be provided to an NLU system so that the system can determine the meanings of sentences in the same way that a person does.

The rule-based approach was widely used in NLU from the mid-1950s through the mid-1990s until machine-learning-based approaches became popular. However, there are still NLU problems where rule-based approaches are useful, either on their own or when combined with other techniques.

We will begin by reviewing the rules and data that are relevant to various aspects of language.

Words and lexicons

Nearly everyone is familiar with...

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

Rule-based approaches

Words and lexicons

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

Rule-based approaches

Words and lexicons

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you