Author: Silvie Cinkova

# Multilingual Stopwords

#### Silvie Cinkova

#### Maciej Eder

# Functions and arguments

The `tidystopwords` package gives you potential stopwords in more than 100 languages. Its main function is `generate_stoplist`. Its argument `language` accepts atomic strings and character vectors of language names or language abbreviations corresponding to those listed by the helping function `list_supported_languages`.

The `list_supported_languages` function comes with three numbered output options.

- Option `1` outputs a character vector of unique word forms.
- Option `2` outputs a named character vector of word forms. The names denote `stop classes` roughly corresponding to parts of speech. Note that, in this output, the word forms are not unique. For instance, in English stopwords, that would occur as a subordinating conjunction as well as as a pronoun.
- Option `3` (the default) outputs a data frame, where each row represents a combination of language (columns `lang_name` and `lang_id`), word form and word lemma (columns `form` and `lemma`), and several other columns explained below.

# Definition of stopwords

The `list_supported_languages` output is based on `multilingual_stoplist` - a data frame that was automatically extracted from the Universal Dependencies treebanks (henceforth UD). Universal Dependencies is a framework for cross-linguistically consistent grammatical annotation. The `tidystopwords` package uses their lemmatization, universal parts of speech, and universal features to derive an inventory of stop classes:

- `abbreviation` (e.g. e.g., cf., etc);
- `adposition` (preposition or postposition e.g. in and ago);
- `auxiliary verb` (e.g. been, have, must);
- `conjunction_subordinator` (e.g. and, because);
- `contraction` (e.g. ’nt);
- `determiner_quantifier` (e.g. third, which, both);
- `interjection` (e.g. yes );
- `particle` (e.g. off in take off )
- `pronominal` (functional words that act as nouns - e.g., him, it. Pronouns acting as adjectives (your) and pronominal adverbs (where) are covered by the `determiner_quantifier` stop class.)

In terms of the Universal Dependencies, the stop classes are defined as follows:

- `abbreviation`: `ufeat` contains `Abbr=Yes` and upos does not equal `NOUN` or `ADJ`;
- `adposition`: `upos` equals `AVP`;
- `auxiliary verb`: `upos` equals `AUX`;
- `conjunction_subordinator`: `upos` equals `CONJ` or `SCONJ`;
- `contraction`: neither `form` nor `lemma` equal `_`, `upos` equals `_` and the form has occurred more than twice in the corpus;
- `determiner_quantifier`: either `upos` equals `DET` or `ufeat` contains `PronType` and at least one of the following strings: `NumType`, `Ind`, `Dem`, `Int`, `Rel`, `Tot`, `Neg`;
- `interjection`: `upos` equals `INTJ`;
- `particle`: `upos` equals `PART`;
- `pronominal`: `upos` equals `PRON` with no restrictions to `ufeat` or `ufeat` contains `PronType` but then `upos` does not equal `DET`.

# Vocabulary coverage

Each version of this package uses the latest UD release available to generate the `multilingual_stoplist` data frame. Therefore `multilingual_stoplist` can differ from version to version. Typically, a new UD release brings bigger annotated corpora and emerging corpora of new languages.

All stopword lists in `tidystopwords` have been generated automatically from the data available at the moment. Hence their quality depends on the size of the underlying corpora as well as the morphological richness of the given language.

To allow the user to assess the reliability of the stopword list for the given language, the `multilingual_stoplist` contains relevant frequency information for each word form in three columns: `n_formlemma`, `n_uposlemma`, and
`n_stopclasses`.

The `n_formlemma` column gives the absolute frequency of the given word form with the given lemma. The `n_uposformlemma` column gives the absolute frequency of the given word form with the given lemma and upos.

The `n_stopclasses` column says in how many stop classes the given word form with the given lemma occurs. For instance that occurs as `determiner_quantifier` (that pie tastes good), `pronominal` (don’t mention that), and `conjunction_subordinator` (say that you will do it).

# Noise control

Even high-quality reference corpora such as the UD treebanks contain tagging errors and typos. A two step frequency filter minimizes the noise: 1) a word form must occur more than three times with a given lemma; 2) if a word form with a given lemma (rendered by `n_formlemma`) occurs in
several different `upos` combinations (`n_uposlemma`), only combinations that represent more than 20% of `n_formlemma` remain listed.