mirror of https://github.com/explosion/spaCy.git
132 lines
4.8 KiB
Plaintext
132 lines
4.8 KiB
Plaintext
//- 💫 DOCS > API > ANNOTATION SPECS
|
||
|
||
include ../_includes/_mixins
|
||
|
||
p This document describes the target annotations spaCy is trained to predict.
|
||
|
||
|
||
+section("tokenization")
|
||
+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.lang.en import English
|
||
nlp = English()
|
||
tokens = nlp('Some\nspaces and\ttab characters')
|
||
tokens_text = [t.text for t in tokens]
|
||
assert tokens_text == ['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.
|
||
|
||
+section("sbd")
|
||
+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.
|
||
|
||
+section("pos-tagging")
|
||
+h(2, "pos-tagging") Part-of-speech Tagging
|
||
|
||
+aside("Tip: Understanding tags")
|
||
| You can also use #[code spacy.explain()] to get the description for the
|
||
| string representation of a tag. For example,
|
||
| #[code spacy.explain("RB")] will return "adverb".
|
||
|
||
include _annotation/_pos-tags
|
||
|
||
+section("lemmatization")
|
||
+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-].
|
||
|
||
+infobox("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 -PRON-], which is used as the lemma for
|
||
| all personal pronouns.
|
||
|
||
+section("dependency-parsing")
|
||
+h(2, "dependency-parsing") Syntactic Dependency Parsing
|
||
|
||
+aside("Tip: Understanding labels")
|
||
| You can also use #[code spacy.explain()] to get the description for the
|
||
| string representation of a label. For example,
|
||
| #[code spacy.explain("prt")] will return "particle".
|
||
|
||
include _annotation/_dep-labels
|
||
|
||
+section("named-entities")
|
||
+h(2, "named-entities") Named Entity Recognition
|
||
|
||
+aside("Tip: Understanding entity types")
|
||
| You can also use #[code spacy.explain()] to get the description for the
|
||
| string representation of an entity label. For example,
|
||
| #[code spacy.explain("LANGUAGE")] will return "any named language".
|
||
|
||
include _annotation/_named-entities
|
||
|
||
+h(3, "biluo") BILUO Scheme
|
||
|
||
include _annotation/_biluo
|
||
|
||
+section("training")
|
||
+h(2, "json-input") JSON input format for training
|
||
|
||
+under-construction
|
||
|
||
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
|
||
}]
|
||
}]
|
||
}
|