Stemming of Token Variables

step_stem() creates a specification of a recipe step that will convert a token variable to have its stemmed version.

Usage

step_stem(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  options = list(),
  custom_stemmer = NULL,
  skip = FALSE,
  id = rand_id("stem")
)

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: Not used by this step since no new variables are created.
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().
options: A list of options passed to the stemmer function.
custom_stemmer: A custom stemming function. If none is provided it will default to "SnowballC".
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

Words tend to have different forms depending on context, such as organize, organizes, and organizing. In many situations it is beneficial to have these words condensed into one to allow for a smaller pool of words. Stemming is the act of chopping off the end of words using a set of heuristics.

Note that the stemming will only be done at the end of the word and will therefore not work reliably on ngrams or sentences.

Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and is_custom_stemmer (indicate if custom stemmer was used).

Case weights

The underlying operation does not allow for case weights.

Examples

library(recipes)
library(modeldata)
data(tate_text)

tate_rec <- recipe(~., data = tate_text) %>%
  step_tokenize(medium) %>%
  step_stem(medium)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [8 tokens]
#> 2 [3 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [3 tokens]
#> # Unique Tokens: 3

tidy(tate_rec, number = 2)
#> # A tibble: 1 × 3
#>   terms  is_custom_stemmer id        
#>   <chr>  <lgl>             <chr>     
#> 1 medium FALSE             stem_8HcUV
tidy(tate_obj, number = 2)
#> # A tibble: 1 × 3
#>   terms  is_custom_stemmer id        
#>   <chr>  <lgl>             <chr>     
#> 1 medium FALSE             stem_8HcUV

# Using custom stemmer. Here a custom stemmer that removes the last letter
# if it is a "s".
remove_s <- function(x) gsub("s$", "", x)

tate_rec <- recipe(~., data = tate_text) %>%
  step_tokenize(medium) %>%
  step_stem(medium, custom_stemmer = remove_s)

tate_obj <- tate_rec %>%
  prep()

bake(tate_obj, new_data = NULL, medium) %>%
  slice(1:2)
#> # A tibble: 2 × 1
#>       medium
#>    <tknlist>
#> 1 [8 tokens]
#> 2 [3 tokens]

bake(tate_obj, new_data = NULL) %>%
  slice(2) %>%
  pull(medium)
#> <textrecipes_tokenlist[1]>
#> [1] [3 tokens]
#> # Unique Tokens: 3