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36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
import numpy as np
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from spacy.attrs import HEAD, DEP
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from spacy.symbols import nsubj, dobj, punct, amod, nmod, conj, cc, root
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from spacy.en import English
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from spacy.syntax.iterators import EnglishNounChunks
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def test_not_nested():
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nlp = English(parser=False, entity=False)
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sent = u'''Peter has chronic command and control issues'''.strip()
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tokens = nlp(sent)
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tokens.from_array(
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[HEAD, DEP],
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np.asarray(
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[
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[1, nsubj],
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[0, root],
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[4, amod],
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[3, nmod],
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[-1, cc],
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[-2, conj],
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[-5, dobj]
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], dtype='int32'))
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tokens.noun_chunks = EnglishNounChunks
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for chunk in tokens.noun_chunks:
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print(chunk.text)
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word_occurred = {}
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for chunk in tokens.noun_chunks:
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for word in chunk:
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word_occurred.setdefault(word.text, 0)
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word_occurred[word.text] += 1
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for word, freq in word_occurred.items():
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assert freq == 1, (word, [chunk.text for chunk in tokens.noun_chunks])
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