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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
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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

More spaCy features

Most of the NLP development is token and span oriented; that is, it processes tags, dependency relations, tokens themselves, and phrases. Most of the time we eliminate small words and words without much meaning; we process URLs differently, and so on. What we do sometimes depends on the token shape (token is a short word or token looks like an URL string) or more semantical features (such as the token is an article, or the token is a conjunction). In this section, we will see these features of tokens with examples. We'll start with features related to the token shape:

 doc = nlp("Hello, hi!")
 doc[0].lower_
'hello'

token.lower_ returns the token in lowercase. The return value is a Unicode string and this feature is equivalent to token.text.lower().

is_lower and is_upper are similar to their Python string method counterparts, islower() and isupper(). is_lower returns True if all the characters are lowercase, while is_upper does the...

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