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145 lines
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145 lines
4.5 KiB
Plaintext
//- 💫 DOCS > API > ANNOTATION SPECS
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include ../../_includes/_mixins
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p This document describes the target annotations spaCy is trained to predict.
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+h(2, "tokenization") Tokenization
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p
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| Tokenization standards are based on the
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| #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
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| The tokenizer differs from most by including tokens for significant
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| whitespace. Any sequence of whitespace characters beyond a single space
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| (#[code ' ']) is included as a token.
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+aside-code("Example").
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from spacy.en import English
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nlp = English(parser=False)
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tokens = nlp('Some\nspaces and\ttab characters')
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print([t.orth_ for t in tokens])
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# ['Some', '\n', 'spaces', ' ', 'and', '\t', 'tab', 'characters']
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p
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| The whitespace tokens are useful for much the same reason punctuation is
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| – it's often an important delimiter in the text. By preserving it in the
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| token output, we are able to maintain a simple alignment between the
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| tokens and the original string, and we ensure that no information is
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| lost during processing.
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+h(2, "sentence-boundary") Sentence boundary detection
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p
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| Sentence boundaries are calculated from the syntactic parse tree, so
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| features such as punctuation and capitalisation play an important but
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| non-decisive role in determining the sentence boundaries. Usually this
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| means that the sentence boundaries will at least coincide with clause
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| boundaries, even given poorly punctuated text.
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+h(2, "pos-tagging") Part-of-speech Tagging
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p
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| The part-of-speech tagger uses the
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| #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] version of
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| the Penn Treebank tag set. We also map the tags to the simpler Google
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| Universal POS Tag set. See
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| #[+src(gh("spaCy", "spacy/tagger.pyx")) tagger.pyx] for details.
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+h(2, "lemmatization") Lemmatization
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p A "lemma" is the uninflected form of a word. In English, this means:
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+list
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+item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
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+item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
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+item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
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+item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
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p
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| The lemmatization data is taken from
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| #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
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| special case for pronouns: all pronouns are lemmatized to the special
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| token #[code -PRON-].
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+h(2, "dependency-parsing") Syntactic Dependency Parsing
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p
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| The parser is trained on data produced by the
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| #[+a("http://www.clearnlp.com") ClearNLP] converter. Details of the
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| annotation scheme can be found
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| #[+a("http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf") here].
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+h(2, "named-entities") Named Entity Recognition
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+table([ "Type", "Description" ])
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+row
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+cell #[code PERSON]
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+cell People, including fictional.
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+row
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+cell #[code NORP]
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+cell Nationalities or religious or political groups.
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+row
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+cell #[code FACILITY]
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+cell Buildings, airports, highways, bridges, etc.
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+row
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+cell #[code ORG]
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+cell Companies, agencies, institutions, etc.
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+row
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+cell #[code GPE]
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+cell Countries, cities, states.
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+row
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+cell #[code LOC]
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+cell Non-GPE locations, mountain ranges, bodies of water.
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+row
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+cell #[code PRODUCT]
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+cell Objects, vehicles, foods, etc. (Not services.)
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+row
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+cell #[code EVENT]
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+cell Named hurricanes, battles, wars, sports events, etc.
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+row
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+cell #[code WORK_OF_ART]
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+cell Titles of books, songs, etc.
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+row
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+cell #[code LANGUAGE]
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+cell Any named language.
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p The following values are also annotated in a style similar to names:
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+table([ "Type", "Description" ])
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+row
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+cell #[code DATE]
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+cell Absolute or relative dates or periods.
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+row
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+cell #[code TIME]
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+cell Times smaller than a day.
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+row
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+cell #[code PERCENT]
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+cell Percentage, including "%".
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+row
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+cell #[code MONEY]
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+cell Monetary values, including unit.
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+row
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+cell #[code QUANTITY]
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+cell Measurements, as of weight or distance.
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+row
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+cell #[code ORDINAL]
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+cell "first", "second", etc.
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+row
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+cell #[code CARDINAL]
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+cell Numerals that do not fall under another type.
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