textrecipes (version 1.1.0)

step_lemma: Lemmatization of Token Variables

Description

step_lemma() creates a specification of a recipe step that will extract the lemmatization of a token variable.

Usage

step_lemma(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("lemma")
)

Value

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

Arguments

recipe

A recipes::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().

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.

Tidying

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

terms

character, the selectors or variables selected

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

Details

This stem doesn't perform lemmatization by itself, but rather lets you extract the lemma attribute of the token variable. To be able to use step_lemma you need to use a tokenization method that includes lemmatization. Currently using the "spacyr" engine in step_tokenize() provides lemmatization and works well with step_lemma.

See Also

step_tokenize() to turn characters into tokens

Other Steps for Token Modification: step_ngram(), step_pos_filter(), step_stem(), step_stopwords(), step_tokenfilter(), step_tokenmerge()

Examples

if (FALSE) {
library(recipes)

short_data <- data.frame(text = c(
  "This is a short tale,",
  "With many cats and ladies."
))

rec_spec <- recipe(~text, data = short_data) %>%
  step_tokenize(text, engine = "spacyr") %>%
  step_lemma(text) %>%
  step_tf(text)

rec_prepped <- prep(rec_spec)

bake(rec_prepped, new_data = NULL)
}