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//- 💫 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") The displaCy #[sup ENT] visualizer
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
| server yourself. To help you do that, we've open-sourced both the
| #[+a(gh("spacy-services")) back-end service] and the
| #[+a(gh("displacy-ent")) front-end client].
+codepen("ALxpQO", 450)
+h(2, "entity-types") Built-in entity types
include ../api/_annotation/_named-entities
+aside("Install")
| The #[+api("load") 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.en.download] 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.
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