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75 lines
3.0 KiB
Plaintext
75 lines
3.0 KiB
Plaintext
include ../../_includes/_mixins
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p.u-text-large spaCy features a rule-matching engine that operates over tokens. The rules can refer to token annotations and flags, and matches support callbacks to accept, modify and/or act on the match. The rule matcher also allows you to associate patterns with entity IDs, to allow some basic entity linking or disambiguation.
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+code("python", "Matcher Example").
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from spacy.matcher import Matcher
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from spacy.attrs import *
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import spacy
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nlp = spacy.load('en', parser=False, entity=False)
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def merge_phrases(matcher, doc, i, matches):
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'''
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Merge a phrase. We have to be careful here because we'll change the token indices.
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To avoid problems, merge all the phrases once we're called on the last match.
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'''
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if i != len(matches)-1:
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return None
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# Get Span objects
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spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
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for ent_id, label, span in spans:
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span.merge(label=label, tag='NNP' if label else span.root.tag_)
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matcher = Matcher(nlp.vocab)
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matcher.add_entity(
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"GoogleNow", # Entity ID -- Helps you act on the match.
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{"ent_type": "PRODUCT", "wiki_en": "Google_Now"}, # Arbitrary attributes (optional)
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acceptor=None, # Accept or modify the match
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on_match=merge_phrases # Callback to act on the matches
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)
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matcher.add_pattern(
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"GoogleNow", # Entity ID -- Created if doesn't exist.
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[ # The pattern is a list of *Token Specifiers*.
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{ # This Token Specifier matches tokens whose orth field is "Google"
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ORTH: "Google"
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},
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{ # This Token Specifier matches tokens whose orth field is "Now"
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ORTH: "Now"
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}
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],
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label=None # Can associate a label to the pattern-match, to handle it better.
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)
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doc = nlp(u"I prefer Siri to Google Now.")
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matches = matcher(doc)
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for ent_id, label, start, end in matches:
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print(nlp.vocab.strings[ent_id], nlp.vocab.strings[label], doc[start : end].text)
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entity = matcher.get_entity(ent_id)
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print(entity)
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matcher.add_pattern(
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"GoogleNow",
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[ # This Surface Form matches "google now", verbatim, and requires
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# "google" to have the NNP tag. This helps prevent the pattern from
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# matching cases like "I will google now to look up the time"
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{
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ORTH: "google",
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TAG: "NNP"
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},
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{
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ORTH: "now"
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}
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]
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)
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doc = nlp(u"I'll google now to find out how the google now service works.")
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matches = matcher(doc)
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for ent_id, label, start, end in matches:
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print(ent_id, label, start, end, doc[start : end].text)
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# Because we specified the on_match=merge_phrases callback,
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# we should see 'google now' as a single token.
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for token in doc:
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print(token.text, token.lemma_, token.tag_, token.ent_type_)
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