mirror of https://github.com/explosion/spaCy.git
Strings has be to on vocab object
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@ -32,7 +32,7 @@ p.u-text-large spaCy features a rule-matching engine that operates over tokens.
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doc = nlp(u"I prefer Siri to Google Now.")
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doc = nlp(u"I prefer Siri to Google Now.")
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matches = matcher(doc)
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matches = matcher(doc)
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for ent_id, label, start, end in matches:
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for ent_id, label, start, end in matches:
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print(nlp.strings[ent_id], nlp.strings[label], doc[start : end].text)
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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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entity = matcher.get_entity(ent_id)
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print(entity)
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print(entity)
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