Visualizing Named Entities

Besides viewing Part of Speech dependencies with style='dep', displaCy offers a style='ent' visualizer:



In [1]:

    
# Perform standard imports
import spacy
nlp = spacy.load('en_core_web_sm')

# Import the displaCy library
from spacy import displacy



In [2]:

    
doc = nlp(u'Over the last quarter Apple sold nearly 20 thousand iPods for a profit of $6 million. '
         u'By contrast, Sony sold only 7 thousand Walkman music players.')

displacy.render(doc, style='ent', jupyter=True)









    




Over 

    the last quarter
    DATE

 

    Apple
    ORG

 sold 

    nearly 20 thousand
    CARDINAL

 

    iPods
    PRODUCT

 for a profit of 

    $6 million
    MONEY

. By contrast, 

    Sony
    ORG

 sold 

    only 7 thousand
    CARDINAL

 

    Walkman
    PRODUCT

 music players.

Viewing Sentences Line by Line

Unlike the displaCy dependency parse, the NER viewer has to take in a Doc object with an ents attribute. For this reason, we can't just pass a list of spans to .render(), we have to create a new Doc from each span.text:



In [3]:

    
for sent in doc.sents:
    displacy.render(nlp(sent.text), style='ent', jupyter=True)









    




Over 

    the last quarter
    DATE

 

    Apple
    ORG

 sold 

    nearly 20 thousand
    CARDINAL

 

    iPods
    PRODUCT

 for a profit of 

    $6 million
    MONEY

.






    




By contrast, 

    Sony
    ORG

 sold 

    only 7 thousand
    CARDINAL

 

    Walkman
    PRODUCT

 music players.

**NOTE**: If a span does not contain any entities, displaCy will issue a harmless warning:



In [4]:

    
doc2 = nlp(u'Over the last quarter Apple sold nearly 20 thousand iPods for a profit of $6 million. '
         u'By contrast, my kids sold a lot of lemonade.')



In [5]:

    
for sent in doc2.sents:
    displacy.render(nlp(sent.text), style='ent', jupyter=True)









    




Over 

    the last quarter
    DATE

 

    Apple
    ORG

 sold 

    nearly 20 thousand
    CARDINAL

 

    iPods
    PRODUCT

 for a profit of 

    $6 million
    MONEY

.






    



C:\Anaconda3\lib\runpy.py:193: UserWarning: [W006] No entities to visualize found in Doc object. If this is surprising to you, make sure the Doc was processed using a model that supports named entity recognition, and check the `doc.ents` property manually if necessary.
  "__main__", mod_spec)






    




By contrast, my kids sold a lot of lemonade.

**WORKAROUND:** We can avert this with an additional bit of code:



In [6]:

    
for sent in doc2.sents:
    docx = nlp(sent.text)
    if docx.ents:
        displacy.render(docx, style='ent', jupyter=True)
    else:
        print(docx.text)









    




Over 

    the last quarter
    DATE

 

    Apple
    ORG

 sold 

    nearly 20 thousand
    CARDINAL

 

    iPods
    PRODUCT

 for a profit of 

    $6 million
    MONEY

.






    



By contrast, my kids sold a lot of lemonade.

Viewing Specific Entities

You can pass a list of entity types to restrict the visualization:



In [7]:

    
options = {'ents': ['ORG', 'PRODUCT']}

displacy.render(doc, style='ent', jupyter=True, options=options)









    




Over the last quarter 

    Apple
    ORG

 sold nearly 20 thousand 

    iPods
    PRODUCT

 for a profit of $6 million. By contrast, 

    Sony
    ORG

 sold only 7 thousand 

    Walkman
    PRODUCT

 music players.

Customizing Colors and Effects

You can also pass background color and gradient options:



In [8]:

    
colors = {'ORG': 'linear-gradient(90deg, #aa9cfc, #fc9ce7)', 'PRODUCT': 'radial-gradient(yellow, green)'}

options = {'ents': ['ORG', 'PRODUCT'], 'colors':colors}

displacy.render(doc, style='ent', jupyter=True, options=options)









    




Over the last quarter 

    Apple
    ORG

 sold nearly 20 thousand 

    iPods
    PRODUCT

 for a profit of $6 million. By contrast, 

    Sony
    ORG

 sold only 7 thousand 

    Walkman
    PRODUCT

 music players.

For more on applying CSS background colors and gradients, visit https://www.w3schools.com/css/css3_gradients.asp

Creating Visualizations Outside of Jupyter

If you're using another Python IDE or writing a script, you can choose to have spaCy serve up HTML separately.

Instead of displacy.render(), use displacy.serve():



In [9]:

    
displacy.serve(doc, style='ent', options=options)









    



    Serving on port 5000...
    Using the 'ent' visualizer







    



127.0.0.1 - - [04/Dec/2018 13:21:26] "GET / HTTP/1.1" 200 2210
127.0.0.1 - - [04/Dec/2018 13:21:26] "GET /favicon.ico HTTP/1.1" 200 2210






    



    Shutting down server on port 5000.

**After running the cell above, click the link below to view the dependency parse**:

http://127.0.0.1:5000

**To shut down the server and return to jupyter**, interrupt the kernel either through the **Kernel** menu above, by hitting the black square on the toolbar, or by typing the keyboard shortcut `Esc`, `I`, `I`

For more on Visualizing the entity recognizer visit https://spacy.io/usage/visualizers#ent

Visualizing Named Entities

Viewing Sentences Line by Line

Viewing Specific Entities

Customizing Colors and Effects

Creating Visualizations Outside of Jupyter

Next Up: Sentence Segmentation