Predicting linguistic annotations
You'll now get to try one of spaCy's pre-trained model packages and see its predictions in action. Feel free to try it out on your own text! The small English model is already available as the variable nlp
.
To find out what a tag or label means, you can call spacy.explain
in the IPython shell. For example: spacy.explain('PROPN')
or spacy.explain('GPE')
.
This exercise is part of the course
Advanced NLP with spaCy
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
text = "It’s official: Apple is the first U.S. public company to reach a $1 trillion market value"
# Process the text
doc = ____
for token in doc:
# Get the token text, part-of-speech tag and dependency label
token_text = ____.____
token_pos = ____.____
token_dep = ____.____
# This is for formatting only
print('{:<12}{:<10}{:<10}'.format(token_text, token_pos, token_dep))