Comparing NLTK with spaCy NER

Using the same text you used in the first exercise of this chapter, you'll now see the results using spaCy's NER annotator. How will they compare?

The article has been pre-loaded as article. To minimize execution times, you'll be asked to specify the keyword argument disable=['tagger', 'parser', 'matcher'] when loading the spaCy model, because you only care about the entity in this exercise.

This exercise is part of the course

Introduction to Natural Language Processing in Python

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Exercise instructions

Import spacy.
Load the 'en_core_web_sm' model using spacy.load(). Specify the additional keyword arguments disable=['tagger', 'parser', 'matcher'].
Create a spacy document object by passing article into nlp().
Using ent as your iterator variable, iterate over the entities of doc and print out the labels (ent.label_) and text (ent.text).

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import spacy
____

# Instantiate the English model: nlp
nlp = ____

# Create a new document: doc
doc = ____

# Print all of the found entities and their labels
for ____ in ____:
    print(____, ____)

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