Text tokenization utility

Vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a vector where the coefficient for each token could be binary, based on word count, based on tf-idf...

text_tokenizer(num_words = NULL,
  filters = "!\"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n",
  lower = TRUE, split = " ", char_level = FALSE, oov_token = NULL)

Arguments

num_words

the maximum number of words to keep, based on word frequency. Only the most common num_words words will be kept.
filters

a string where each element is a character that will be filtered from the texts. The default is all punctuation, plus tabs and line breaks, minus the ' character.
lower

boolean. Whether to convert the texts to lowercase.
split

character or string to use for token splitting.
char_level

if TRUE, every character will be treated as a token
oov_token

NULL or string If given, it will be added to `word_index`` and used to replace out-of-vocabulary words during text_to_sequence calls.

Details

By default, all punctuation is removed, turning the texts into space-separated sequences of words (words maybe include the ' character). These sequences are then split into lists of tokens. They will then be indexed or vectorized. 0 is a reserved index that won't be assigned to any word.

Attributes

The tokenizer object has the following attributes:

  • word_counts --- named list mapping words to the number of times they appeared on during fit. Only set after fit_text_tokenizer() is called on the tokenizer.

  • word_docs --- named list mapping words to the number of documents/texts they appeared on during fit. Only set after fit_text_tokenizer() is called on the tokenizer.

  • word_index --- named list mapping words to their rank/index (int). Only set after fit_text_tokenizer() is called on the tokenizer.

  • document_count --- int. Number of documents (texts/sequences) the tokenizer was trained on. Only set after fit_text_tokenizer() is called on the tokenizer.

See also

Other text tokenization: fit_text_tokenizer, save_text_tokenizer, sequences_to_matrix, texts_to_matrix, texts_to_sequences_generator, texts_to_sequences