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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

Overview of spaCy conventions

Every NLP application consists of several steps of processing the text. As you can see in the first chapter, we have always created instances called nlp and doc. But what did we do exactly?

When we call nlp on our text, spaCy applies some processing steps. The first step is tokenization to produce a Doc object. The Doc object is then processed further with a tagger, a parser, and an entity recognizer. This way of processing the text is called a language processing pipeline. Each pipeline component returns the processed Doc and then passes it to the next component:

Figure 2.1 – A high-level view of the processing pipeline

A spaCy pipeline object is created when we load a language model. We load an English model and initialize a pipeline in the following code segment:

 import spacy
 nlp = spacy.load("en_core_web_md")
 doc = nlp("I went there")

What happened exactly in the preceding code is as follows...

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