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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20. Other Books You May Enjoy

Exploring the libraries

In this section, we will review several of the major Python libraries that are used in NLP; specifically, NLTK, spaCy, and Keras. These are very useful libraries, and they can perform most basic NLP tasks. However, as you gain experience with NLP, you will also find additional NLP libraries that may be appropriate for specific tasks as well, and you are encouraged to explore those.

Using NLTK

NLTK (https://www.nltk.org/) is a very popular open source Python library that greatly reduces the effort involved in developing natural language applications by providing support for many frequently performed tasks. NLTK also includes many corpora (sets of ready-to-use natural language texts) that can be used for exploring NLP problems and testing algorithms.

In this section, we will go over what NLTK can do, and then discuss the NLTK installation process.

As we discussed in Chapter 3, many distinct tasks can be performed in an NLU pipeline as the processing...

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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 the libraries

Using NLTK

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 the libraries

Using NLTK

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Authors (1)

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