Data structures best practices

The code in this example is trying to analyze a text and collect all proper nouns. If the token following the proper noun is a verb, it should also be extracted. A doc object has already been created.

# Get all tokens and part-of-speech tags
pos_tags = [token.pos_ for token in doc]

for index, pos in enumerate(pos_tags):
    # Check if the current token is a proper noun
    if pos == 'PROPN':
        # Check if the next token is a verb
        if pos_tags[index + 1] == 'VERB':
            print('Found a verb after a proper noun!')

Question

Why is the code bad?

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The tokens in the result should be converted back to Token objects. This will let you reuse them in spaCy.
It only uses lists of strings instead of native token attributes. This is often less efficient, and can't express complex relationships.
pos_ is the wrong attribute to use for extracting proper nouns. You should use tag_ and the 'NNP' and 'NNS' labels instead.

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Data structures best practices

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