mirror of https://github.com/explosion/spaCy.git
88 lines
4.1 KiB
Python
88 lines
4.1 KiB
Python
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# coding: utf-8
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from __future__ import unicode_literals
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from spacy.lang.en import English
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from spacy.matcher import PhraseMatcher
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from spacy.tokens.doc import Doc
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from spacy.tokens.span import Span
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from spacy.tokens.token import Token
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class TechCompanyRecognizer(object):
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"""Example of a spaCy v2.0 pipeline component that sets entity annotations
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based on list of single or multiple-word company names. Companies are
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labelled as ORG and their spans are merged into one token. Additionally,
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._.has_tech_org and ._.is_tech_org is set on the Doc/Span and Token
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respectively."""
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name = 'tech_companies' # component name, will show up in the pipeline
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def __init__(self, nlp, companies=tuple(), label='ORG'):
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"""Initialise the pipeline component. The shared nlp instance is used
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to initialise the matcher with the shared vocab, get the label ID and
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generate Doc objects as phrase match patterns.
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"""
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self.label = nlp.vocab.strings[label] # get entity label ID
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# Set up the PhraseMatcher – it can now take Doc objects as patterns,
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# so even if the list of companies is long, it's very efficient
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patterns = [nlp(org) for org in companies]
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self.matcher = PhraseMatcher(nlp.vocab)
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self.matcher.add('TECH_ORGS', None, *patterns)
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# Register attribute on the Token. We'll be overwriting this based on
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# the matches, so we're only setting a default value, not a getter.
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Token.set_extension('is_tech_org', default=False)
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# Register attributes on Doc and Span via a getter that checks if one of
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# the contained tokens is set to is_tech_org == True.
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Doc.set_extension('has_tech_org', getter=self.has_tech_org)
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Span.set_extension('has_tech_org', getter=self.has_tech_org)
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def __call__(self, doc):
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"""Apply the pipeline component on a Doc object and modify it if matches
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are found. Return the Doc, so it can be processed by the next component
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in the pipeline, if available.
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"""
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matches = self.matcher(doc)
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spans = [] # keep the spans for later so we can merge them afterwards
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for _, start, end in matches:
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# Generate Span representing the entity & set label
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entity = Span(doc, start, end, label=self.label)
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spans.append(entity)
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# Set custom attribute on each token of the entity
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for token in entity:
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token._.set('is_tech_org', True)
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# Overwrite doc.ents and add entity – be careful not to replace!
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doc.ents = list(doc.ents) + [entity]
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for span in spans:
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# Iterate over all spans and merge them into one token. This is done
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# after setting the entities – otherwise, it would cause mismatched
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# indices!
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span.merge()
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return doc # don't forget to return the Doc!
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def has_tech_org(self, tokens):
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"""Getter for Doc and Span attributes. Returns True if one of the tokens
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is a tech org. Since the getter is only called when we access the
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attribute, we can refer to the Token's 'is_tech_org' attribute here,
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which is already set in the processing step."""
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return any([t._.get('is_tech_org') for t in tokens])
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# For simplicity, we start off with only the blank English Language class and
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# no model or pre-defined pipeline loaded.
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nlp = English()
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companies = ['Alphabet Inc.', 'Google', 'Netflix', 'Apple'] # etc.
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component = TechCompanyRecognizer(nlp, companies) # initialise component
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nlp.add_pipe(component, last=True) # add it to the pipeline as the last element
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doc = nlp(u"Alphabet Inc. is the company behind Google.")
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print('Pipeline', nlp.pipe_names) # pipeline contains component name
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print('Tokens', [t.text for t in doc]) # company names from the list are merged
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print('Doc has_tech_org', doc._.has_tech_org) # Doc contains tech orgs
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print('Token 0 is_tech_org', doc[0]._.is_tech_org) # "Alphabet Inc." is a tech org
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print('Token 1 is_tech_org', doc[1]._.is_tech_org) # "is" is not
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print('Entities', [(e.text, e.label_) for e in doc.ents]) # all orgs are entities
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