mirror of https://github.com/explosion/spaCy.git
267 lines
11 KiB
Plaintext
267 lines
11 KiB
Plaintext
//- 💫 DOCS > USAGE > NAMED ENTITY RECOGNITION
|
||
|
||
include ../../_includes/_mixins
|
||
|
||
p
|
||
| spaCy features an extremely fast statistical entity recognition system,
|
||
| that assigns labels to contiguous spans of tokens. The default model
|
||
| identifies a variety of named and numeric entities, including companies,
|
||
| locations, organizations and products. You can add arbitrary classes to
|
||
| the entity recognition system, and update the model with new examples.
|
||
|
||
+h(2, "101") Named Entity Recognition 101
|
||
+tag-model("named entities")
|
||
|
||
include _spacy-101/_named-entities
|
||
|
||
+h(2, "accessing") Accessing entity annotations
|
||
|
||
p
|
||
| The standard way to access entity annotations is the
|
||
| #[+api("doc#ents") #[code doc.ents]] property, which produces a sequence
|
||
| of #[+api("span") #[code Span]] objects. The entity type is accessible
|
||
| either as an integer ID or as a string, using the attributes
|
||
| #[code ent.label] and #[code ent.label_]. The #[code Span] object acts
|
||
| as a sequence of tokens, so you can iterate over the entity or index into
|
||
| it. You can also get the text form of the whole entity, as though it were
|
||
| a single token.
|
||
|
||
p
|
||
| You can also access token entity annotations using the
|
||
| #[+api("token#attributes") #[code token.ent_iob]] and
|
||
| #[+api("token#attributes") #[code token.ent_type]] attributes.
|
||
| #[code token.ent_iob] indicates whether an entity starts, continues or
|
||
| ends on the tag. If no entity type is set on a token, it will return an
|
||
| empty string.
|
||
|
||
+aside("IOB Scheme")
|
||
| #[code I] – Token is inside an entity.#[br]
|
||
| #[code O] – Token is outside an entity.#[br]
|
||
| #[code B] – Token is the beginning of an entity.#[br]
|
||
|
||
+code("Example").
|
||
doc = nlp(u'San Francisco considers banning sidewalk delivery robots')
|
||
|
||
# document level
|
||
ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
|
||
assert ents == [(u'San Francisco', 0, 13, u'GPE')]
|
||
|
||
# token level
|
||
ent_san = [doc[0].text, doc[0].ent_iob_, doc[0].ent_type_]
|
||
ent_francisco = [doc[1].text, doc[1].ent_iob_, doc[1].ent_type_]
|
||
assert ent_san == [u'San', u'B', u'GPE']
|
||
assert ent_francisco == [u'Francisco', u'I', u'GPE']
|
||
|
||
+table(["Text", "ent_iob", "ent.iob_", "ent_type", "ent_type_", "Description"])
|
||
- var style = [0, 1, 1, 1, 1, 0]
|
||
+annotation-row(["San", 3, "B", 381, "GPE", "beginning of an entity"], style)
|
||
+annotation-row(["Francisco", 1, "I", 381, "GPE", "inside an entity"], style)
|
||
+annotation-row(["considers", 2, "O", 0, '""', "outside an entity"], style)
|
||
+annotation-row(["banning", 2, "O", 0, '""', "outside an entity"], style)
|
||
+annotation-row(["sidewalk", 2, "O", 0, '""', "outside an entity"], style)
|
||
+annotation-row(["delivery", 2, "O", 0, '""', "outside an entity"], style)
|
||
+annotation-row(["robots", 2, "O", 0, '""', "outside an entity"], style)
|
||
|
||
+h(2, "setting") Setting entity annotations
|
||
|
||
p
|
||
| To ensure that the sequence of token annotations remains consistent, you
|
||
| have to set entity annotations #[strong at the document level]. However,
|
||
| you can't write directly to the #[code token.ent_iob] or
|
||
| #[code token.ent_type] attributes, so the easiest way to set entities is
|
||
| to assign to the #[+api("doc#ents") #[code doc.ents]] attribute
|
||
| and create the new entity as a #[+api("span") #[code Span]].
|
||
|
||
+code("Example").
|
||
from spacy.tokens import Span
|
||
|
||
doc = nlp(u'Netflix is hiring a new VP of global policy')
|
||
# the model didn't recognise any entities :(
|
||
|
||
ORG = doc.vocab.strings[u'ORG'] # get integer ID of entity label
|
||
netflix_ent = Span(doc, 0, 1, label=ORG) # create a Span for the new entity
|
||
doc.ents = [netflix_ent]
|
||
|
||
ents = [(e.text, e.start_char, e.end_char, e.label_) for e in doc.ents]
|
||
assert ents = [(u'Netflix', 0, 7, u'ORG')]
|
||
|
||
p
|
||
| Keep in mind that you need to create a #[code Span] with the start and
|
||
| end index of the #[strong token], not the start and end index of the
|
||
| entity in the document. In this case, "Netflix" is token #[code (0, 1)] –
|
||
| but at the document level, the entity will have the start and end
|
||
| indices #[code (0, 7)].
|
||
|
||
+h(3, "setting-from-array") Setting entity annotations from array
|
||
|
||
p
|
||
| You can also assign entity annotations using the
|
||
| #[+api("doc#from_array") #[code doc.from_array()]] method. To do this,
|
||
| you should include both the #[code ENT_TYPE] and the #[code ENT_IOB]
|
||
| attributes in the array you're importing from.
|
||
|
||
+code.
|
||
import numpy
|
||
from spacy.attrs import ENT_IOB, ENT_TYPE
|
||
|
||
doc = nlp.make_doc(u'London is a big city in the United Kingdom.')
|
||
assert list(doc.ents) == []
|
||
|
||
header = [ENT_IOB, ENT_TYPE]
|
||
attr_array = numpy.zeros((len(doc), len(header)))
|
||
attr_array[0, 0] = 2 # B
|
||
attr_array[0, 1] = doc.vocab.strings[u'GPE']
|
||
doc.from_array(header, attr_array)
|
||
assert list(doc.ents)[0].text == u'London'
|
||
|
||
+h(3, "setting-cython") Setting entity annotations in Cython
|
||
|
||
p
|
||
| Finally, you can always write to the underlying struct, if you compile
|
||
| a #[+a("http://cython.org/") Cython] function. This is easy to do, and
|
||
| allows you to write efficient native code.
|
||
|
||
+code.
|
||
# cython: infer_types=True
|
||
from spacy.tokens.doc cimport Doc
|
||
|
||
cpdef set_entity(Doc doc, int start, int end, int ent_type):
|
||
for i in range(start, end):
|
||
doc.c[i].ent_type = ent_type
|
||
doc.c[start].ent_iob = 3
|
||
for i in range(start+1, end):
|
||
doc.c[i].ent_iob = 2
|
||
|
||
p
|
||
| Obviously, if you write directly to the array of #[code TokenC*] structs,
|
||
| you'll have responsibility for ensuring that the data is left in a
|
||
| consistent state.
|
||
|
||
+h(2, "entity-types") Built-in entity types
|
||
|
||
+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 ../api/_annotation/_named-entities
|
||
|
||
+h(2, "updating") Training and updating
|
||
|
||
p
|
||
| To provide training examples to the entity recogniser, you'll first need
|
||
| to create an instance of the #[+api("goldparse") #[code GoldParse]] class.
|
||
| You can specify your annotations in a stand-off format or as token tags.
|
||
|
||
+code.
|
||
import random
|
||
import spacy
|
||
from spacy.gold import GoldParse
|
||
from spacy.pipeline import EntityRecognizer
|
||
|
||
train_data = [('Who is Chaka Khan?', [(7, 17, 'PERSON')]),
|
||
('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])]
|
||
|
||
nlp = spacy.load('en', entity=False, parser=False)
|
||
ner = EntityRecognizer(nlp.vocab, entity_types=['PERSON', 'LOC'])
|
||
|
||
for itn in range(5):
|
||
random.shuffle(train_data)
|
||
for raw_text, entity_offsets in train_data:
|
||
doc = nlp.make_doc(raw_text)
|
||
gold = GoldParse(doc, entities=entity_offsets)
|
||
|
||
nlp.tagger(doc)
|
||
ner.update(doc, gold)
|
||
ner.model.end_training()
|
||
|
||
p
|
||
| If a character offset in your entity annotations don't fall on a token
|
||
| boundary, the #[code GoldParse] class will treat that annotation as a
|
||
| missing value. This allows for more realistic training, because the
|
||
| entity recogniser is allowed to learn from examples that may feature
|
||
| tokenizer errors.
|
||
|
||
+aside-code("Example").
|
||
doc = Doc(nlp.vocab, [u'rats', u'make', u'good', u'pets'])
|
||
gold = GoldParse(doc, [u'U-ANIMAL', u'O', u'O', u'O'])
|
||
ner = EntityRecognizer(nlp.vocab, entity_types=['ANIMAL'])
|
||
ner.update(doc, gold)
|
||
|
||
p
|
||
| You can also provide token-level entity annotation, using the
|
||
| following tagging scheme to describe the entity boundaries:
|
||
|
||
+table([ "Tag", "Description" ])
|
||
+row
|
||
+cell #[code #[span.u-color-theme B] EGIN]
|
||
+cell The first token of a multi-token entity.
|
||
|
||
+row
|
||
+cell #[code #[span.u-color-theme I] N]
|
||
+cell An inner token of a multi-token entity.
|
||
|
||
+row
|
||
+cell #[code #[span.u-color-theme L] AST]
|
||
+cell The final token of a multi-token entity.
|
||
|
||
+row
|
||
+cell #[code #[span.u-color-theme U] NIT]
|
||
+cell A single-token entity.
|
||
|
||
+row
|
||
+cell #[code #[span.u-color-theme O] UT]
|
||
+cell A non-entity token.
|
||
|
||
+aside("Why BILUO, not IOB?")
|
||
| There are several coding schemes for encoding entity annotations as
|
||
| token tags. These coding schemes are equally expressive, but not
|
||
| necessarily equally learnable.
|
||
| #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
|
||
| showed that the minimal #[strong Begin], #[strong In], #[strong Out]
|
||
| scheme was more difficult to learn than the #[strong BILUO] scheme that
|
||
| we use, which explicitly marks boundary tokens.
|
||
|
||
p
|
||
| spaCy translates the character offsets into this scheme, in order to
|
||
| decide the cost of each action given the current state of the entity
|
||
| recogniser. The costs are then used to calculate the gradient of the
|
||
| loss, to train the model. The exact algorithm is a pastiche of
|
||
| well-known methods, and is not currently described in any single
|
||
| publication. The model is a greedy transition-based parser guided by a
|
||
| linear model whose weights are learned using the averaged perceptron
|
||
| loss, via the #[+a("http://www.aclweb.org/anthology/C12-1059") dynamic oracle]
|
||
| imitation learning strategy. The transition system is equivalent to the
|
||
| BILOU tagging scheme.
|
||
|
||
+h(2, "displacy") Visualizing named entities
|
||
|
||
p
|
||
| The #[+a(DEMOS_URL + "/displacy-ent/") displaCy #[sup ENT] visualizer]
|
||
| lets you explore an entity recognition model's behaviour interactively.
|
||
| If you're training a model, it's very useful to run the visualization
|
||
| yourself. To help you do that, spaCy v2.0+ comes with a visualization
|
||
| module. Simply pass a #[code Doc] or a list of #[code Doc] objects to
|
||
| displaCy and run #[+api("displacy#serve") #[code displacy.serve]] to
|
||
| run the web server, or #[+api("displacy#render") #[code displacy.render]]
|
||
| to generate the raw markup.
|
||
|
||
p
|
||
| For more details and examples, see the
|
||
| #[+a("/docs/usage/visualizers") usage workflow on visualizing spaCy].
|
||
|
||
+code("Named Entity example").
|
||
import spacy
|
||
from spacy import displacy
|
||
|
||
text = """But Google is starting from behind. The company made a late push
|
||
into hardware, and Apple’s Siri, available on iPhones, and Amazon’s Alexa
|
||
software, which runs on its Echo and Dot devices, have clear leads in
|
||
consumer adoption."""
|
||
|
||
nlp = spacy.load('custom_ner_model')
|
||
doc = nlp(text)
|
||
displacy.serve(doc, style='ent')
|
||
|
||
+codepen("a73f8b68f9af3157855962b283b364e4", 345)
|