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
Exercise instructions
- Import
spacy
. - Load the
'en_core_web_sm'
model usingspacy.load()
. Specify the additional keyword argumentsdisable=['tagger', 'parser', 'matcher']
. - Create a
spacy
document object by passingarticle
intonlp()
. - Using
ent
as your iterator variable, iterate over the entities ofdoc
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(____, ____)