Compute the semantic similarity between two text variables.

Compute the semantic similarity between two text variables.

textSimilarity(x, y, method = "cosine", center = TRUE, scale = FALSE)

Arguments

x

Word embeddings from textEmbed.

y

Word embeddings from textEmbed.

method

Character string describing type of measure to be computed. Default is "cosine" (see also "spearmen", "pearson" as well as measures from textDistance() (which here is computed as 1 - textDistance) including "euclidean", "maximum", "manhattan", "canberra", "binary" and "minkowski").

center

(boolean; from base::scale) If center is TRUE then centering is done by subtracting the column means (omitting NAs) of x from their corresponding columns, 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) columns of x by their standard deviations if center is TRUE, and the root mean square otherwise.

Value

A vector comprising semantic similarity scores.

See also

see textDistance, textSimilarityNorm

Examples

library(dplyr)
similarity_scores <- textSimilarity(
  x = word_embeddings_4$texts$harmonytext,
  y = word_embeddings_4$texts$satisfactiontext
)
comment(similarity_scores)
#> [1] "x embedding = .Information about the embeddings. textEmbedRawLayers: model: bert-base-uncased ; layers: 11 ; word_type_embeddings: TRUE ; max_token_to_sentence: 4 ; text_version: 0.9.99. textEmbedLayerAggregation: layers =  11 aggregation_from_layers_to_tokens =  concatenate aggregation_from_tokens_to_texts =  mean tokens_select =   tokens_deselect =  .y embedding = .Information about the embeddings. textEmbedRawLayers: model: bert-base-uncased ; layers: 11 ; word_type_embeddings: TRUE ; max_token_to_sentence: 4 ; text_version: 0.9.99. textEmbedLayerAggregation: layers =  11 aggregation_from_layers_to_tokens =  concatenate aggregation_from_tokens_to_texts =  mean tokens_select =   tokens_deselect =  .method = .cosine.center = .TRUE.scale = .FALSE"