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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
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from __future__ import unicode_literals
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import spacy
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from spacy.attrs import ORTH
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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('NNP' if label else span.root.tag_, span.text, doc.vocab.strings[label])
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def test_entity_ID_assignment():
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nlp = spacy.en.English()
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text = u"""The golf club is broken"""
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doc = nlp(text)
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golf_pattern = [
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{ ORTH: "golf"},
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{ ORTH: "club"}
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]
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matcher = spacy.matcher.Matcher(nlp.vocab)
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matcher.add_entity('Sport_Equipment', on_match = merge_phrases)
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matcher.add_pattern("Sport_Equipment", golf_pattern, label = 'Sport_Equipment')
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match = matcher(doc)
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entities = list(doc.ents)
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assert entities != [] #assertion 1
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assert entities[0].label != 0 #assertion 2
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