textDistanceMatrix: Compute semantic distance scores between all combinations in...

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

Compute semantic distance scores between all combinations in a word embedding

Description

Compute semantic distance scores between all combinations in a word embedding

Usage

textDistanceMatrix(x, method = "euclidean", center = FALSE, scale = FALSE)

Arguments

x	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 matrix of semantic distance scores

See Also

see textDistanceNorm

Examples

distance_scores <- textDistanceMatrix(word_embeddings_4$texts$harmonytext[1:3, ])
round(distance_scores, 3)

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