step_pos_filter creates a specification of a recipe step that will filter a tokenlist based on part of speech tags.

step_pos_filter(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  keep_tags = "NOUN",
  skip = FALSE,
  id = rand_id("pos_filter")
)

# S3 method for step_pos_filter
tidy(x, ...)

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 variables. For step_pos_filter, this indicates the variables to be encoded into a tokenlist. See recipes::selections() for more details. For the tidy method, these are not currently used.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the recipe has been baked.

columns

A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep.recipe().

keep_tags

Character variable of part of speech tags to keep. See details for complete list of tags. Defaults to "NOUN".

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 = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

x

A step_pos_filter object.

Value

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

Details

Possible part of speech tags for spacyr engine are: "ADJ", "ADP", "ADV", "AUX", "CONJ", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X" and "SPACE". For more information look here https://spacy.io/api/annotation#pos-tagging.

See also

step_tokenize() to turn character into tokenlist.

Other tokenlist to tokenlist steps: step_lemma(), step_ngram(), step_stem(), step_stopwords(), step_tokenfilter(), step_tokenmerge()

Examples

# \dontrun{ library(recipes) short_data <- data.frame(text = c("This is a short tale,", "With many cats and ladies.")) okc_rec <- recipe(~ text, data = short_data) %>% step_tokenize(text, engine = "spacyr") %>% step_pos_filter(text, keep_tags = "NOUN") %>% step_tf(text) okc_obj <- prep(okc_rec) juice(okc_obj)
#> # A tibble: 2 x 3 #> tf_text_cats tf_text_ladies tf_text_tale #> <dbl> <dbl> <dbl> #> 1 0 0 1 #> 2 1 1 0
# }