mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
112 lines
3.9 KiB
Plaintext
112 lines
3.9 KiB
Plaintext
//- 💫 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
|
||
|
||
include _annotation/_pos-tags
|
||
|
||
+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"
|
||
|
||
+aside("About spaCy's custom pronoun lemma")
|
||
| Unlike verbs and common nouns, there's no clear base form of a personal
|
||
| pronoun. Should the lemma of "me" be "I", or should we normalize person
|
||
| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
|
||
| novel symbol, #[code.u-nowrap -PRON-], which is used as the lemma for
|
||
| all personal pronouns.
|
||
|
||
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
|
||
|
||
+table(["Language", "Converter", "Scheme"])
|
||
+row
|
||
+cell English
|
||
+cell #[+a("http://www.clearnlp.com") ClearNLP]
|
||
+cell #[+a("http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf") CLEAR Style]
|
||
|
||
+row
|
||
+cell German
|
||
+cell #[+a("https://github.com/wbwseeker/tiger2dep") TIGER]
|
||
+cell #[+a("http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/index.html") TIGER]
|
||
|
||
+h(2, "named-entities") Named Entity Recognition
|
||
|
||
include _annotation/_named-entities
|
||
|
||
+h(2, "json-input") JSON input format for training
|
||
|
||
p
|
||
| spaCy takes training data in the following format:
|
||
|
||
+code("Example structure").
|
||
doc: {
|
||
id: string,
|
||
paragraphs: [{
|
||
raw: string,
|
||
sents: [int],
|
||
tokens: [{
|
||
start: int,
|
||
tag: string,
|
||
head: int,
|
||
dep: string
|
||
}],
|
||
ner: [{
|
||
start: int,
|
||
end: int,
|
||
label: string
|
||
}],
|
||
brackets: [{
|
||
start: int,
|
||
end: int,
|
||
label: string
|
||
}]
|
||
}]
|
||
}
|