mirror of https://github.com/explosion/spaCy.git
240 lines
9.2 KiB
Plaintext
240 lines
9.2 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.
|
||
|
||
+aside-code("Example").
|
||
import spacy
|
||
nlp = spacy.load('en')
|
||
doc = nlp(u'London is a big city in the United Kingdom.')
|
||
for ent in doc.ents:
|
||
print(ent.label_, ent.text)
|
||
# GPE London
|
||
# GPE United Kingdom
|
||
|
||
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. See the #[+api("span") API reference] for more details.
|
||
|
||
p
|
||
| You can access token entity annotations using the #[code token.ent_iob]
|
||
| and #[code token.ent_type] attributes. The #[code token.ent_iob]
|
||
| attribute indicates whether an entity starts, continues or ends on the
|
||
| tag (In, Begin, Out).
|
||
|
||
+code("Example").
|
||
doc = nlp(u'London is a big city in the United Kingdom.')
|
||
print(doc[0].text, doc[0].ent_iob, doc[0].ent_type_)
|
||
# (u'London', 2, u'GPE')
|
||
print(doc[1].text, doc[1].ent_iob, doc[1].ent_type_)
|
||
# (u'is', 3, u'')
|
||
|
||
+h(2, "setting") Setting entity annotations
|
||
|
||
p
|
||
| To ensure that the sequence of token annotations remains consistent, you
|
||
| have to set entity annotations at the document level — you can't write
|
||
| directly to the #[code token.ent_iob] or #[code token.ent_type]
|
||
| attributes. The easiest way to set entities is to assign to the
|
||
| #[code doc.ents] attribute.
|
||
|
||
+code("Example").
|
||
doc = nlp(u'London is a big city in the United Kingdom.')
|
||
doc.ents = []
|
||
assert doc[0].ent_type_ == ''
|
||
doc.ents = [Span(doc, 0, 1, label=doc.vocab.strings['GPE'])]
|
||
assert doc[0].ent_type_ == 'GPE'
|
||
doc.ents = []
|
||
doc.ents = [(u'LondonCity', doc.vocab.strings['GPE'], 0, 1)]
|
||
|
||
p
|
||
| The value you assign should be a sequence, the values of which
|
||
| can either be #[code Span] objects, or #[code (ent_id, ent_type, start, end)]
|
||
| tuples, where #[code start] and #[code end] are token offsets that
|
||
| describe the slice of the document that should be annotated.
|
||
|
||
p
|
||
| You can also assign entity annotations using the #[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("Example").
|
||
from spacy.attrs import ENT_IOB, ENT_TYPE
|
||
import numpy
|
||
|
||
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'
|
||
|
||
p
|
||
| Finally, you can always write to the underlying struct, if you compile
|
||
| a Cython function. This is easy to do, and allows you to write efficient
|
||
| native code.
|
||
|
||
+code("Example").
|
||
# 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, "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)
|
||
|
||
+h(2, "entity-types") Built-in entity types
|
||
|
||
include ../api/_annotation/_named-entities
|
||
|
||
+aside("Install")
|
||
| The #[+api("load") #[code spacy.load()]] function configures a pipeline that
|
||
| includes all of the available annotators for the given ID. In the example
|
||
| above, the #[code 'en'] ID tells spaCy to load the default English
|
||
| pipeline. If you have installed the data with
|
||
| #[code python -m spacy download en], this will include the entity
|
||
| recognition model.
|
||
|
||
+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 #[code GoldParse] class. You can specify
|
||
| your annotations in a stand-off format or as token tags.
|
||
|
||
+code.
|
||
import spacy
|
||
import random
|
||
from spacy.gold import GoldParse
|
||
from spacy.language 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.
|