textDistance: Compute the semantic distance between two text variables.

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

Compute the semantic distance between two text variables.

Description

Compute the semantic distance between two text variables.

Usage

textDistance(x, y, method = "euclidean", center = FALSE, scale = FALSE)

Arguments

x	Word embeddings (from textEmbed).
y	Word embeddings (from textEmbed).
method	(character) Character string describing type of measure to be computed; default is "euclidean" (see also measures from stats:dist() including "maximum", "manhattan", "canberra", "binary" and "minkowski". It is also possible to use "cosine", which computes the cosine distance (i.e., 1 - cosine(x, y)).
center	(boolean; from base::scale) If center is TRUE then centering is done by subtracting the embedding mean (omitting NAs) of x from each of its dimension, and if center is FALSE, no centering is done.
scale	(boolean; from base::scale) If scale is TRUE then scaling is done by dividing the (centered) embedding dimensions by the standard deviation of the embedding if center is TRUE, and the root mean square otherwise.

Value

A vector comprising semantic distance scores.

See Also

See textSimilarity and textSimilarityNorm.

Examples

# Compute the semantic distance score between the embeddings
# from "harmonytext" and "satisfactiontext".

## Not run: 
distance_scores <- textDistance(
  x = word_embeddings_4$texts$harmonytext,
  y = word_embeddings_4$texts$satisfactiontext
)

# Show information about how distance_scores were constructed.

comment(distance_scores)

## End(Not run)

text documentation built on Sept. 11, 2024, 7:22 p.m.