//- 💫 DOCS > API > ANNOTATION SPECS

include ../../_includes/_mixins

p This document describes the target annotations spaCy is trained to predict.

+h(2, "tokenization") Tokenization

p
    |  Tokenization standards are based on the
    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
    |  The tokenizer differs from most by including tokens for significant
    |  whitespace. Any sequence of whitespace characters beyond a single space
    |  (#[code ' ']) is included as a token.

+aside-code("Example").
    from spacy.en import English
    nlp = English(parser=False)
    tokens = nlp('Some\nspaces  and\ttab characters')
    print([t.orth_ for t in tokens])
    # ['Some', '\n', 'spaces', ' ', 'and', '\t', 'tab', 'characters']

p
    |  The whitespace tokens are useful for much the same reason punctuation is
    |  – it's often an important delimiter in the text. By preserving it in the
    |  token output, we are able to maintain a simple alignment between the
    |  tokens and the original string, and we ensure that no information is
    |  lost during processing.

+h(2, "sentence-boundary") Sentence boundary detection

p
    |  Sentence boundaries are calculated from the syntactic parse tree, so
    |  features such as punctuation and capitalisation play an important but
    |  non-decisive role in determining the sentence boundaries. Usually this
    |  means that the sentence boundaries will at least coincide with clause
    |  boundaries, even given poorly punctuated text.

+h(2, "pos-tagging") Part-of-speech Tagging

p
    |  The part-of-speech tagger uses the
    |  #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] version of
    |  the Penn Treebank tag set. We also map the tags to the simpler Google
    |  Universal POS Tag set. See
    |  #[+src(gh("spaCy", "spacy/tagger.pyx")) tagger.pyx] for details.

+h(2, "lemmatization") Lemmatization

p A "lemma" is the uninflected form of a word. In English, this means:

+list
    +item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
    +item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
    +item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
    +item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"

p
    |  The lemmatization data is taken from
    |  #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
    |  special case for pronouns: all pronouns are lemmatized to the special
    |  token #[code -PRON-].

+h(2, "dependency-parsing") Syntactic Dependency Parsing

p
    |  The parser is trained on data produced by the
    |  #[+a("http://www.clearnlp.com") ClearNLP] converter. Details of the
    |  annotation scheme can be found
    |  #[+a("http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf") here].

+h(2, "named-entities") Named Entity Recognition

+table(["Entity Type", "Description"])
    +row
        +cell #[code PERSON]
        +cell People, including fictional.

    +row
        +cell #[code NORP]
        +cell Nationalities or religious or political groups.

    +row
        +cell #[code FAC]
        +cell Facilities, such as buildings, airports, highways, bridges, etc.

    +row
        +cell #[code ORG]
        +cell Companies, agencies, institutions, etc.

    +row
        +cell #[code GPE]
        +cell Countries, cities, states.

    +row
        +cell #[code LOC]
        +cell Non-GPE locations, mountain ranges, bodies of water.

    +row
        +cell #[code PRODUCT]
        +cell Vehicles, weapons, foods, etc. (Not services)

    +row
        +cell #[code EVENT]
        +cell Named hurricanes, battles, wars, sports events, etc.

    +row
        +cell #[code WORK_OF_ART]
        +cell Titles of books, songs, etc.

    +row
        +cell #[code LAW]
        +cell Named documents made into laws

    +row
        +cell #[code LANGUAGE]
        +cell Any named language

p The following values are also annotated in a style similar to names:

+table(["Entity Type", "Description"])
    +row
        +cell #[code DATE]
        +cell Absolute or relative dates or periods

    +row
        +cell #[code TIME]
        +cell Times smaller than a day

    +row
        +cell #[code PERCENT]
        +cell Percentage (including “%”)

    +row
        +cell #[code MONEY]
        +cell Monetary values, including unit

    +row
        +cell #[code QUANTITY]
        +cell Measurements, as of weight or distance

    +row
        +cell #[code ORDINAL]
        +cell "first", "second"

    +row
        +cell #[code CARDINAL]
        +cell Numerals that do not fall under another type