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32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from ...attrs import HEAD, DEP
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from ...symbols import nsubj, dobj, amod, nmod, conj, cc, root
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from ...syntax.iterators import english_noun_chunks
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from ..util import get_doc
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import numpy
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def test_doc_noun_chunks_not_nested(en_tokenizer):
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text = "Peter has chronic command and control issues"
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heads = [1, 0, 4, 3, -1, -2, -5]
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deps = ['nsubj', 'ROOT', 'amod', 'nmod', 'cc', 'conj', 'dobj']
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=heads, deps=deps)
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tokens.from_array(
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[HEAD, DEP],
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numpy.asarray([[1, nsubj], [0, root], [4, amod], [3, nmod], [-1, cc],
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[-2, conj], [-5, dobj]], dtype='uint64'))
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tokens.noun_chunks_iterator = english_noun_chunks
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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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