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Semantic similarity for categorizing text

The main objective of semantic similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. In this exercise, you will find similar sentences to the word sauce from an example text in Amazon Fine Food Reviews. You can use spacy to calculate the similarity score of the word sauce and any of the sentences in a given texts string and report the most similar sentence's score.

A texts string is pre-loaded that contains all reviews' Text data. You'll use en_core_web_md English model for this exercise which is already available as nlp.

Diese Übung ist Teil des Kurses

Natural Language Processing with spaCy

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Anleitung zur Übung

  • Use nlp to generate Doc containers for the word sauce and for texts and store them at key and sentences respectively.
  • Calculate similarity scores of the word sauce with each sentence in the texts string (rounded to two digits).

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Populate Doc containers for the word "sauce" and for "texts" string
key = ____
sentences = ____

# Calculate similarity score of each sentence and a Doc container for the word sauce
semantic_scores = []
for sent in sentences.____:
	semantic_scores.append({"score": round(sent.____(____), 2)})
print(semantic_scores)
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