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Similar words in a vocabulary

Finding semantically similar terms has various applications in information retrieval. In this exercise, you will practice finding the most semantically similar term to the word computer from the en_core_web_md model's vocabulary.

The computer word vector is already extracted and stored as word_vector. The en_core_web_md model is already loaded as nlp, and NumPy package is loaded as np.

You can use the .most_similar() function of the nlp.vocab.vectors object to find the most semantically similar terms. Using [0][0] to index the output of this function will return the word IDs of the semantically similar terms. nlp.vocab.strings[<a given word>] can be used to find the word ID of a given word and it can similarly return the word associated with a given word ID.

This exercise is part of the course

Natural Language Processing with spaCy

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Exercise instructions

  • Find the most semantically similar term from the en_core_web_md vocabulary.
  • Find the list of similar words given the word IDs of the similar terms.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Find the most similar word to the word computer
most_similar_words = nlp.vocab.vectors.____(np.asarray([____]), n = 1)

# Find the list of similar words given the word IDs
words = [nlp.____.____[____] for w in most_similar_words[0][0]]
print(words)
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