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Doc similarity with spaCy

Semantic similarity is the process of analyzing multiple sentences to identify similarities between them. In this exercise, you will practice calculating semantic similarities of documents to a given document. The goal is to categorize a list of given reviews that are relevant to canned dog food.

The canned dog food category is stored at category. A sample of five food reviews has been provided for you in a list called texts. en_core_web_md is loaded as nlp.

Diese Übung ist Teil des Kurses

Natural Language Processing with spaCy

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Anleitung zur Übung

  • Create a documents list containing Doc containers of all texts.
  • Create a Doc container of the category and store it as category_document.
  • Iterate through documents and print the similarity scores of each Doc container and the category_document, rounded to three digits.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Create a documents list containing Doc containers
documents = [____ for t in texts]

# Create a Doc container of the category
category = "canned dog food"
category_document = ____(____)

# Print similarity scores of each Doc container and the category_document
for i, doc in enumerate(documents):
  print(f"Semantic similarity with document {i+1}:", round(doc.____(____), 3))
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