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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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Toc

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

EntityRuler

While covering Matcher, we saw that we can extract named entities with Matcher by using the ENT_TYPE attribute. We recall from the previous chapter that ENT_TYPE is a linguistic attribute that refers to the entity type of the token, such as person, place, or organization. Let's see an example:

pattern = [{"ENT_TYPE": "PERSON"}]
matcher.add("personEnt",  [pattern])
doc = nlp("Bill Gates visited Berlin.")
matches = matcher(doc)
for mid, start, end in matches:
    print(start, end, doc[start:end])
... 
0 1 Bill
1 2 Gates

Again, we created a Matcher object called matcher and called it on the Doc object, doc. The result is two tokens, Bill and Gates; Matcher always matches at the token level. We got Bill and Gates, instead of the full entity, Bill Gates. If you want to get the full entity rather than the individual tokens, you can do this:

pattern = [{"ENT_TYPE": "PERSON"...
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