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https://github.com/explosion/spaCy.git
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Modernise noun chunks tests and don't depend on models
This commit is contained in:
Ines Montani
parent
3ee97b5686
commit
49febd8c62
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@ -1,137 +1,76 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from ..util import get_doc
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import pytest
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import numpy
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from ...attrs import HEAD, DEP
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@pytest.mark.models
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class TestNounChunks:
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@pytest.fixture(scope="class")
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def ex1_en(self, EN):
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example = EN.tokenizer.tokens_from_list('A base phrase should be recognized .'.split(' '))
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EN.tagger.tag_from_strings(example, 'DT NN NN MD VB VBN .'.split(' '))
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det,compound,nsubjpass,aux,auxpass,root,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubjpass','aux','auxpass','root','punct'] )
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example.from_array([HEAD, DEP],
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numpy.asarray(
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[
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[2, det],
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[1, compound],
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[3, nsubjpass],
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[2, aux],
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[1, auxpass],
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[0, root],
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[-1, punct]
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], dtype='int32'))
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return example
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def test_parser_noun_chunks_standard(en_tokenizer):
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text = "A base phrase should be recognized."
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heads = [2, 1, 3, 2, 1, 0, -1]
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tags = ['DT', 'JJ', 'NN', 'MD', 'VB', 'VBN', '.']
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deps = ['det', 'amod', 'nsubjpass', 'aux', 'auxpass', 'ROOT', 'punct']
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@pytest.fixture(scope="class")
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def ex2_en(self, EN):
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example = EN.tokenizer.tokens_from_list('A base phrase and a good phrase are often the same .'.split(' '))
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EN.tagger.tag_from_strings(example, 'DT NN NN CC DT JJ NN VBP RB DT JJ .'.split(' '))
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det,compound,nsubj,cc,amod,conj,root,advmod,attr,punct = tuple( EN.vocab.strings[l] for l in ['det','compound','nsubj','cc','amod','conj','root','advmod','attr','punct'] )
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example.from_array([HEAD, DEP],
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numpy.asarray(
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[
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[2, det],
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[1, compound],
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[5, nsubj],
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[-1, cc],
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[1, det],
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[1, amod],
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[-4, conj],
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[0, root],
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[-1, advmod],
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[1, det],
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[-3, attr],
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[-4, punct]
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], dtype='int32'))
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return example
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags, deps=deps, heads=heads)
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chunks = list(doc.noun_chunks)
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assert len(chunks) == 1
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assert chunks[0].text_with_ws == "A base phrase "
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@pytest.fixture(scope="class")
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def ex3_en(self, EN):
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example = EN.tokenizer.tokens_from_list('A phrase with another phrase occurs .'.split(' '))
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EN.tagger.tag_from_strings(example, 'DT NN IN DT NN VBZ .'.split(' '))
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det,nsubj,prep,pobj,root,punct = tuple( EN.vocab.strings[l] for l in ['det','nsubj','prep','pobj','root','punct'] )
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example.from_array([HEAD, DEP],
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numpy.asarray(
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[
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[1, det],
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[4, nsubj],
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[-1, prep],
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[1, det],
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[-2, pobj],
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[0, root],
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[-1, punct]
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], dtype='int32'))
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return example
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@pytest.fixture(scope="class")
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def ex1_de(self, DE):
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example = DE.tokenizer.tokens_from_list('Eine Tasse steht auf dem Tisch .'.split(' '))
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DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN $.'.split(' '))
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nk,sb,root,mo,punct = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct'])
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example.from_array([HEAD, DEP],
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numpy.asarray(
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[
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[1, nk],
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[1, sb],
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[0, root],
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[-1, mo],
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[1, nk],
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[-2, nk],
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[-3, punct]
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], dtype='int32'))
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return example
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def test_parser_noun_chunks_coordinated(en_tokenizer):
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text = "A base phrase and a good phrase are often the same."
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heads = [2, 1, 5, -1, 2, 1, -4, 0, -1, 1, -3, -4]
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tags = ['DT', 'NN', 'NN', 'CC', 'DT', 'JJ', 'NN', 'VBP', 'RB', 'DT', 'JJ', '.']
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deps = ['det', 'compound', 'nsubj', 'cc', 'det', 'amod', 'conj', 'ROOT', 'advmod', 'det', 'attr', 'punct']
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@pytest.fixture(scope="class")
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def ex2_de(self, DE):
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example = DE.tokenizer.tokens_from_list('Die Sängerin singt mit einer Tasse Kaffee Arien .'.split(' '))
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DE.tagger.tag_from_strings(example, 'ART NN VVFIN APPR ART NN NN NN $.'.split(' '))
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nk,sb,root,mo,punct,oa = tuple( DE.vocab.strings[l] for l in ['nk','sb','root','mo','punct','oa'])
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example.from_array([HEAD, DEP],
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numpy.asarray(
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[
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[1, nk],
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[1, sb],
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[0, root],
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[-1, mo],
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[1, nk],
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[-2, nk],
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[-1, nk],
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[-5, oa],
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[-6, punct]
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], dtype='int32'))
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return example
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags, deps=deps, heads=heads)
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chunks = list(doc.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].text_with_ws == "A base phrase "
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assert chunks[1].text_with_ws == "a good phrase "
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def test_en_standard_chunk(self, ex1_en):
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chunks = list(ex1_en.noun_chunks)
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assert len(chunks) == 1
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assert chunks[0].string == 'A base phrase '
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def test_en_coordinated_chunks(self, ex2_en):
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chunks = list(ex2_en.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].string == 'A base phrase '
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assert chunks[1].string == 'a good phrase '
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def test_parser_noun_chunks_pp_chunks(en_tokenizer):
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text = "A phrase with another phrase occurs."
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heads = [1, 4, -1, 1, -2, 0, -1]
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tags = ['DT', 'NN', 'IN', 'DT', 'NN', 'VBZ', '.']
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deps = ['det', 'nsubj', 'prep', 'det', 'pobj', 'ROOT', 'punct']
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def test_en_pp_chunks(self, ex3_en):
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chunks = list(ex3_en.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].string == 'A phrase '
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assert chunks[1].string == 'another phrase '
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tokens = en_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags, deps=deps, heads=heads)
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chunks = list(doc.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].text_with_ws == "A phrase "
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assert chunks[1].text_with_ws == "another phrase "
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def test_de_standard_chunk(self, ex1_de):
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chunks = list(ex1_de.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].string == 'Eine Tasse '
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assert chunks[1].string == 'dem Tisch '
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def test_de_extended_chunk(self, ex2_de):
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chunks = list(ex2_de.noun_chunks)
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assert len(chunks) == 3
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assert chunks[0].string == 'Die Sängerin '
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assert chunks[1].string == 'einer Tasse Kaffee '
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assert chunks[2].string == 'Arien '
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def test_parser_noun_chunks_standard_de(de_tokenizer):
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text = "Eine Tasse steht auf dem Tisch."
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heads = [1, 1, 0, -1, 1, -2, -4]
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tags = ['ART', 'NN', 'VVFIN', 'APPR', 'ART', 'NN', '$.']
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deps = ['nk', 'sb', 'ROOT', 'mo', 'nk', 'nk', 'punct']
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tokens = de_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags, deps=deps, heads=heads)
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chunks = list(doc.noun_chunks)
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assert len(chunks) == 2
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assert chunks[0].text_with_ws == "Eine Tasse "
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assert chunks[1].text_with_ws == "dem Tisch "
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def test_de_extended_chunk(de_tokenizer):
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text = "Die Sängerin singt mit einer Tasse Kaffee Arien."
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heads = [1, 1, 0, -1, 1, -2, -1, -5, -6]
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tags = ['ART', 'NN', 'VVFIN', 'APPR', 'ART', 'NN', 'NN', 'NN', '$.']
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deps = ['nk', 'sb', 'ROOT', 'mo', 'nk', 'nk', 'nk', 'oa', 'punct']
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tokens = de_tokenizer(text)
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doc = get_doc(tokens.vocab, [t.text for t in tokens], tags=tags, deps=deps, heads=heads)
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chunks = list(doc.noun_chunks)
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assert len(chunks) == 3
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assert chunks[0].text_with_ws == "Die Sängerin "
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assert chunks[1].text_with_ws == "einer Tasse Kaffee "
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assert chunks[2].text_with_ws == "Arien "
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