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POS tagging with spaCy

In this exercise, you will practice POS tagging. POS tagging is a useful tool in NLP as it allows algorithms to understand the grammatical structure of a sentence and to confirm words that have multiple meanings such as watch and play.

For this exercise, en_core_web_sm has been loaded for you as nlp. Three comments from the Airline Travel Information System (ATIS) dataset have been provided for you in a list called texts.

Este ejercicio forma parte del curso

Natural Language Processing with spaCy

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Instrucciones del ejercicio

  • Compile documents, a list of all doc containers for each text in texts list using list comprehension.
  • For each doc container, print each token's text and its corresponding POS tag by iterating through documents and tokens of each doc container using a nested for loop.

Ejercicio interactivo práctico

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# Compile a list of all Doc containers of texts
documents = [____(text) for text in texts]

# Print token texts and POS tags for each Doc container
for doc in documents:
    for ____ in doc:
        print("Text: ", ____, "| POS tag: ", ____)
    print("\n")
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