tokenize
Parse texts and separate them into lists of tokens (words, lemmas, etc.).
A column of texts to separate into tokens.
An (optional) column identifying the languages of the corresponding texts. It is used to identify the correct model (spaCy)
to use for each text. If the dataset doesn’t contain such a column yet, it can be created using the infer_language step.
Ideally, languages should be expressed as two-letter
ISO 639-1 language codes, such as “en”, “es” or “de” for
English, Spanish or German respectively. We also detect fully spelled out names such as “english”, “German”, “allemande”
etc., but it is not guaranteed that we will recognize all possible spellings correctly always, so ISO codes should be
preferred.
Alternatively, if all texts are in the same language, it can be identified with the language parameter instead.
A column of lists containing the tokens extracted from the texts.
Whether to enable support for additional languages. By default, Arabic (“ar”), Catalan (“ca”), Basque (“eu”), and Turkish (“tu”) are not enabled, since they’re supported only by a different class of language models (stanfordNLP’s Stanza) that is much slower than the rest. This parameter can be used to enable them.
Minimum number (or proportion) of texts to include a language in processing. Any texts in a language with fewer documents than these will be ignored. Can be useful to speed up processing when there is noise in the input languages, and when ignoring languages with a small number of documents only is acceptable. Values smaller than 1 will be interpreted as a proportion of all texts, and values greater than or equal to 1 as an absolute number of documents.
The language of inputs texts.
If all texts are in the same language, it can be specified here instead of passing it as an input column. The language will be used to identify the correct spaCy model to parse and analyze the texts. For allowed values, see the comment regarding the lang column above.
Configure how tokens are extracted and represented in the output. Define the kinds of tokens to extract, how to represent them, and their minimum or maximum frequency in the dataset to be included in the result.
Representation of the individual tokens to extract. I.e. whether verbatim (text/ortho), lower(-case) or lemmatized. Also see spaCy’s attribute reference in this table for further information.
Values must be one of the following:
orthlemmalowertext
Which kinds of tokens to exclude.
Valid filters are stop words (stops), URLs (urls), punctuation (punct), digits,
tokens containing non-alphabetic characters (non_alpha), and tokens containing non-ascii
characters (non_ascii).
Token frequency filter. Filters tokens based on the number of texts they occur in.
Minimum number of rows. Tokens not occurring in at least these many rows (texts) will be excluded.
Values must be in the following range:
0 ≤ min_rows < inf
Maximum proportion of rows. Tokens occurring in more than this proportion of rows (texts) will be excluded.
Values must be in the following range:
0 ≤ max_rows ≤ 1
Keep n most frequent tokens.
Whether to always include the n most frequent tokens, independent of the other filter parameters.
Set to null to ignore.
Values must be in the following range:
0 ≤ keep_top_n < inf
Exclude n most frequent tokens. Even if they passed the other filter conditions.
Values must be in the following range:
0 ≤ filter_top_n < inf
Filter per language. Apply filter conditions separately to texts grouped by language, rather than across all texts.
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