Dependency parsing with spaCy
Dependency parsing analyzes the grammatical structure in a sentence and finds out related words as well as the type of relationship between them. An application of dependency parsing is to identify a sentence object and subject. In this exercise, you will practice extracting dependency labels for given texts.
Three comments from the Airline Travel Information System (ATIS) dataset have been provided for you in a list called texts. en_core_web_sm model is already loaded and available for your use as nlp.
This exercise is part of the course
Natural Language Processing with spaCy
Exercise instructions
- Create a
documentslist containing thedoccontainers of each element in thetextslist. - Print a tuple of (the token's text, dependency label, and label's explanation) per each
doccontainer.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a list of Doc containts of texts list
documents = [____ for t in ____]
# Print each token's text, dependency label and its explanation
for doc in documents:
print([(token.____, token.____, spacy.____(token.____)) for token in doc], "\n")