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