Pretrained Word Embeddings of Tokens

step_word_embeddings() creates a specification of a recipe step that will convert a token variable into word-embedding dimensions by aggregating the vectors of each token from a pre-trained embedding.

Usage

step_word_embeddings(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  columns = NULL,
  embeddings,
  aggregation = c("sum", "mean", "min", "max"),
  aggregation_default = 0,
  prefix = "wordembed",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("word_embeddings")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose which variables are affected by the step. See recipes::selections() for more details.
role: For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
columns: A character string of variable names that will be populated (eventually) by the terms argument. This is NULL until the step is trained by recipes::prep.recipe().
embeddings: A tibble of pre-trained word embeddings, such as those returned by the embedding_glove function from the textdata package. The first column should contain tokens, and additional columns should contain embeddings vectors.
aggregation: A character giving the name of the aggregation function to use. Must be one of "sum", "mean", "min", and "max". Defaults to "sum".
aggregation_default: A numeric denoting the default value for case with no words are matched in embedding. Defaults to 0.
prefix: A character string that will be the prefix to the resulting new variables. See notes below.
keep_original_cols: A logical to keep the original variables in the output. Defaults to FALSE.
skip: A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = FALSE.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

Word embeddings map words (or other tokens) into a high-dimensional feature space. This function maps pre-trained word embeddings onto the tokens in your data.

The argument embeddings provides the pre-trained vectors. Each dimension present in this tibble becomes a new feature column, with each column aggregated across each row of your text using the function supplied in the aggregation argument.

The new components will have names that begin with prefix, then the name of the aggregation function, then the name of the variable from the embeddings tibble (usually something like "d7"). For example, using the default "wordembedding" prefix, and the GloVe embeddings from the textdata package (where the column names are d1, d2, etc), new columns would be wordembedding_d1, wordembedding_d1, etc.

Tidying

When you tidy() this step, a tibble is returned with columns terms, embedding_rows, aggregation, and id:

terms: character, the selectors or variables selected
embedding_rows: integer, number of rows in embedding
aggregation: character,aggregation
id: character, id of this step

Case weights

The underlying operation does not allow for case weights.

Examples

library(recipes)

embeddings <- tibble(
  tokens = c("the", "cat", "ran"),
  d1 = c(1, 0, 0),
  d2 = c(0, 1, 0),
  d3 = c(0, 0, 1)
)

sample_data <- tibble(
  text = c(
    "The.",
    "The cat.",
    "The cat ran."
  ),
  text_label = c("fragment", "fragment", "sentence")
)

rec <- recipe(text_label ~ ., data = sample_data) %>%
  step_tokenize(text) %>%
  step_word_embeddings(text, embeddings = embeddings)

obj <- rec %>%
  prep()

bake(obj, sample_data)
#> # A tibble: 3 × 4
#>   text_label wordembed_text_d1 wordembed_text_d2 wordembed_text_d3
#>   <fct>                  <dbl>             <dbl>             <dbl>
#> 1 fragment                   1                 0                 0
#> 2 fragment                   1                 1                 0
#> 3 sentence                   1                 1                 1

tidy(rec, number = 2)
#> # A tibble: 1 × 4
#>   terms embeddings_rows aggregation id                   
#>   <chr>           <int> <chr>       <chr>                
#> 1 text                3 sum         word_embeddings_kVUCl
tidy(obj, number = 2)
#> # A tibble: 1 × 4
#>   terms embeddings_rows aggregation id                   
#>   <chr>           <int> <chr>       <chr>                
#> 1 text                3 sum         word_embeddings_kVUCl