A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to…

The correct answer is A. Embeddings. Generating embeddings transforms each sentence into a dense numerical vector in a semantic space, where conceptually similar sentences lie close together, enabling straightforward similarity calculations (e.g., cosine similarity) to group or identify related sentences.

Machine Learning

Question

A data scientist is attempting to identify sentences that are conceptually similar to each other within a set of text files. Which of the following is the best way to prepare the data set to accomplish this task after data ingestion?

Options

AEmbeddings
BExtrapolation
CSampling
DOne-hot encoding

How the community answered

(17 responses)

A
82% (14)
B
12% (2)
C
6% (1)

Explanation

Generating embeddings transforms each sentence into a dense numerical vector in a semantic space, where conceptually similar sentences lie close together, enabling straightforward similarity calculations (e.g., cosine similarity) to group or identify related sentences.

Topics

#embeddings#NLP#semantic similarity#text preprocessing

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