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Extract mentions

NLP ยท text

Parse texts and extract any mentions detected.

A "mention", i.e. a reference to a user or account, here simply means any word starting with the "@" character. The step generates a new column with one list of mentions for each original text (row).


The following are the step's expected inputs and outputs and their specific types.

Step signature
    text: text,
    lang: category, 
        "param": value
) -> (mentions: list[category])

where the object {"param": value} is optional in most cases and if present may contain any of the parameters described in the corresponding section below.


Without configuring the languages to be processed simply use the following code. Otherwise see parameters below.

Example call (in recipe editor)
extract_mentions(ds.text, ds.lang) -> (ds.mentions)


text: column:text

A text column to extract mentions from.

lang: column:category

A column identifying the languages of the corresponding texts. 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.


mentions: column:list[category]

A column of lists containing the mentions extracted from the texts.


extended_language_support: boolean = False

Whether to enable support for additional languages. By default, Catalan and Basque are not enabled, since they're supported only by a different class of language models that is much slower than the rest. This parameter can be used to enable them.

min_language_freq: number | integer = 0.02

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.

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