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').

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Advanced NLP with spaCy

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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))

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