spaCy/website/docs/usage/_spacy-101/_language-data.jade

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//- 💫 DOCS > USAGE > SPACY 101 > LANGUAGE DATA
p
| Every language is different and usually full of
| #[strong exceptions and special cases], especially amongst the most
| common words. Some of these exceptions are shared across languages, while
| others are #[strong entirely specific] usually so specific that they need
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| to be hard-coded. The #[+src(gh("spaCy", "spacy/lang")) lang] module
| contains all language-specific data, organised in simple Python files.
| This makes the data easy to update and extend.
p
| The #[strong shared language data] in the directory root includes rules
| that can be generalised across languages for example, rules for basic
| punctuation, emoji, emoticons, single-letter abbreviations and norms for
| equivalent tokens with different spellings, like #[code "] and
| #[code ”]. This helps the models make more accurate predictions.
| The #[strong individual language data] in a submodule contains
| rules that are only relevant to a particular language. It also takes
| care of putting together all components and creating the #[code Language]
| subclass for example, #[code English] or #[code German].
+aside-code.
from spacy.lang.en import English
from spacy.lang.en import German
nlp_en = English() # includes English data
nlp_de = German() # includes German data
+image
include ../../../assets/img/docs/language_data.svg
.u-text-right
+button("/assets/img/docs/language_data.svg", false, "secondary").u-text-tag View large graphic
+table(["Name", "Description"])
+row
+cell #[strong Stop words]#[br]
| #[+src(gh("spacy-dev-resources", "templates/new_language/stop_words.py")) stop_words.py]
+cell
| List of most common words of a language that are often useful to
| filter out, for example "and" or "I". Matching tokens will
| return #[code True] for #[code is_stop].
+row
+cell #[strong Tokenizer exceptions]#[br]
| #[+src(gh("spacy-dev-resources", "templates/new_language/tokenizer_exceptions.py")) tokenizer_exceptions.py]
+cell
| Special-case rules for the tokenizer, for example, contractions
| like "can't" and abbreviations with punctuation, like "U.K.".
+row
+cell #[strong Norm exceptions]
| #[+src(gh("spaCy", "spacy/lang/norm_exceptions.py")) norm_exceptions.py]
+cell
| Special-case rules for normalising tokens to improve the model's
| predictions, for example on American vs. British spelling.
+row
+cell #[strong Punctuation rules]
| #[+src(gh("spaCy", "spacy/lang/punctuation.py")) punctuation.py]
+cell
| Regular expressions for splitting tokens, e.g. on punctuation or
| special characters like emoji. Includes rules for prefixes,
| suffixes and infixes.
+row
+cell #[strong Character classes]
| #[+src(gh("spaCy", "spacy/lang/char_classes.py")) char_classes.py]
+cell
| Character classes to be used in regular expressions, for example,
| latin characters, quotes, hyphens or icons.
+row
+cell #[strong Lexical attributes]
| #[+src(gh("spacy-dev-resources", "templates/new_language/lex_attrs.py")) lex_attrs.py]
+cell
| Custom functions for setting lexical attributes on tokens, e.g.
| #[code like_num], which includes language-specific words like "ten"
| or "hundred".
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+row
+cell #[strong Syntax iterators]
| #[+src(gh("spaCy", "spacy/lang/en/syntax_iterators.py")) syntax_iterators.py]
+cell
| Functions that compute views of a #[code Doc] object based on its
| syntax. At the moment, only used for
| #[+a("/docs/usage/dependency-parse#noun-chunks") noun chunks].
+row
+cell #[strong Lemmatizer]
| #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) lemmatizer.py]
+cell
| Lemmatization rules or a lookup-based lemmatization table to
| assign base forms, for example "be" for "was".
+row
+cell #[strong Tag map]#[br]
| #[+src(gh("spacy-dev-resources", "templates/new_language/tag_map.py")) tag_map.py]
+cell
| Dictionary mapping strings in your tag set to
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
| tags.
+row
+cell #[strong Morph rules]
| #[+src(gh("spaCy", "spacy/lang/en/morph_rules.py")) morph_rules.py]
+cell
| Exception rules for morphological analysis of irregular words like
| personal pronouns.