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