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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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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

Entity extraction

In this section, we'll implement the first step of our chatbot NLU pipeline and extract entities from the dataset utterances. The following are the entities marked in our dataset:

city
date
time
phone_number
cuisine
restaurant_name
street_address

To extract the entities, we'll use the spaCy NER model and the spaCy Matcher class. Let's get started by extracting the city entities.

Extracting city entities

We'll first extract the city entities. We'll get started by recalling some information about the spaCy NER model and entity labels from Chapter 3, Linguistic Features, and Chapter 6, Putting Everything Together: Semantic Parsing with spaCy:  

  • First, we recall that the spaCy named entity label for cities and countries is GPE. Let's ask spaCy to explain what GPE label corresponds to once again:
    import spacy
    nlp = spacy.load("en_core_web_md")
    spacy.explain("GPE")
    'Countries, cities, states...
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