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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781838987312
Length 284 pages
Edition 1st Edition
Languages
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Author (1):
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Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Building Chatbots 8. Chapter 8: Visualizing Text Data 9. Other Books You May Enjoy

Visualizing NER

To visualize named entities, we will again use displacy, the same visualization engine that we used for the visualization of dependency parses.

Getting ready

For this recipe, you will need spacy. If you don't have it installed, install it using the following command:

pip install spacy

How to do it…

We will use spacy to parse the sentence and then the displacy engine to visualize the named entities. The steps are as follows:

  1. Import both spacy and displacy:
    import spacy
    from spacy import displacy
  2. Load the spacy engine:
    nlp = spacy.load('en_core_web_sm')
  3. Define the visualize function, which will create the dependency parse visualization:
    def visualize(doc):
        colors = {"ORG":"green", "PERSON":"yellow"}
        options = {"colors": colors}
        displacy.serve(doc, style='ent', options=options)
  4. Define a...
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