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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Selecting Libraries and Tools for Natural Language Understanding

This chapter will get you set up to process natural language. We will begin by discussing how to install Python, and then we will discuss general software development tools such as JupyterLab and GitHub. We will also review major Python natural language processing (NLP) libraries, including the Natural Language Toolkit (NLTK), spaCy, and TensorFlow/Keras.

Natural language understanding (NLU) technology has benefited from a wide assortment of very capable, freely available tools. While these tools are very powerful, there is no one library that can do all of the NLP tasks needed for all applications, so it is important to understand what the strengths of the different libraries are and how to combine them.

Making the best use of these tools will greatly accelerate any NLU development project. These tools include the Python language itself, development tools such as JupyterLab, and a number of specific natural language...

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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

Selecting Libraries and Tools for Natural Language Understanding

Authors (1)

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Table of Contents (21) Chapters

Selecting Libraries and Tools for Natural Language Understanding

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