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Exercise

word vectors with spaCy

In this exercise you'll get your first experience with word vectors! You're going to use the ATIS dataset, which contains thousands of sentences from real people interacting with a flight booking system.

The user utterances are available in the list sentences, and the corresponding intents in labels.

Your job is to create a 2D array X with as many rows as there are sentences in the dataset, where each row is a vector describing that sentence.

Instructions
100 XP
  • Load the spaCy English model by calling spacy.load() with argument 'en'.
  • Calculate the length of sentences using len() and the dimensionality of the word vectors using nlp.vocab.vectors_length.
  • For each sentence, call the nlp object with the sentence as the sole argument. Store the result as doc.
  • Use the .vector attribute of doc to get the vector representation of each sentence, and store this vector in the appropriate row of X.
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