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Mastering spaCy

You're reading from   Mastering spaCy An end-to-end practical guide to implementing NLP applications using the Python ecosystem

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781800563353
Length 356 pages
Edition 1st Edition
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Author (1):
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Duygu Altınok Duygu Altınok
Author Profile Icon Duygu Altınok
Duygu Altınok
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Getting Started with spaCy
2. Chapter 1: Getting Started with spaCy FREE CHAPTER 3. Chapter 2: Core Operations with spaCy 4. Section 2: spaCy Features
5. Chapter 3: Linguistic Features 6. Chapter 4: Rule-Based Matching 7. Chapter 5: Working with Word Vectors and Semantic Similarity 8. Chapter 6: Putting Everything Together: Semantic Parsing with spaCy 9. Section 3: Machine Learning with spaCy
10. Chapter 7: Customizing spaCy Models 11. Chapter 8: Text Classification with spaCy 12. Chapter 9: spaCy and Transformers 13. Chapter 10: Putting Everything Together: Designing Your Chatbot with spaCy 14. Other Books You May Enjoy

Token-based matching

So far, we've explored the sophisticated linguistic concepts that require statistical models and their usages with spaCy. Some NLU tasks can be solved in tricky ways without the help of any statistical model. One of those ways is regex, which we use to match a predefined set of patterns to our text.

A regex (a regular expression) is a sequence of characters that specifies a search pattern. A regex describes a set of strings that follows the specified pattern. A regex can include letters, digits, and characters with special meanings, such as ?, ., and *. Python's built-in library provides great support to define and match regular expressions. There's another Python 3 library called regex that aims wants to replace re in the future.

Readers who are actively developing NLP applications with Python have definitely come across regex code and, even better, have written regex themselves.

What does a regex look like, then? The following regex matches...

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