def func(nlp):
if nlp.meta['name'] in excluded_nlps:
return
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
assert rules_analyzer.is_independent_noun(doc[referred_index]) or \
rules_analyzer.is_potential_anaphor(doc[referred_index])
assert rules_analyzer.is_potential_anaphor(doc[referring_index])
referred_mention = Mention(doc[referred_index],
include_dependent_siblings)
self.assertEqual(
expected_truth,
rules_analyzer.is_potential_anaphoric_pair(
referred_mention, doc[referring_index], True),
nlp.meta['name'])
self.assertEqual(
expected_reflexive_truth,
rules_analyzer.is_potential_reflexive_pair(
referred_mention, doc[referring_index]), nlp.meta['name'])
self.assertEqual(
is_reflexive_anaphor_truth,
rules_analyzer.is_reflexive_anaphor(doc[referring_index]),
nlp.meta['name'])
def func(nlp):
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
self.assertEqual(
expected_dependent_siblings,
str(doc[index]._.coref_chains.temp_dependent_siblings),
nlp.meta['name'])
for sibling in (sibling for sibling in
doc[index]._.coref_chains.temp_dependent_siblings
if sibling.i != index):
self.assertEqual(doc[index],
sibling._.coref_chains.temp_governing_sibling,
nlp.meta['name'])
if expected_governing_sibling is None:
self.assertEqual(
None, doc[index]._.coref_chains.temp_governing_sibling,
nlp.meta['name'])
else:
self.assertEqual(
doc[expected_governing_sibling],
doc[index]._.coref_chains.temp_governing_sibling,
nlp.meta['name'])
self.assertEqual(
expected_has_or_coordination,
doc[index]._.coref_chains.temp_has_or_coordination,
nlp.meta['name'])
def func(nlp):
if nlp.meta['name'] in excluded_nlps:
return
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
self.assertEqual(expected_truth,
rules_analyzer.is_potentially_referring_back_noun(doc[index]),
nlp.meta['name'])
def func(nlp):
if nlp.meta['name'] in excluded_nlps:
return
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
per_indexes = [token.i for token in doc if
rules_analyzer.is_independent_noun(token)]
self.assertEqual(expected_per_indexes, per_indexes, nlp.meta['name'])
def func(nlp):
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
ancestor = rules_analyzer.get_ancestor_spanning_any_preposition(
doc[index])
if ancestor is None:
self.assertEqual(expected_index, None)
else:
self.assertEqual(expected_index, ancestor.i)
def func(nlp):
if nlp.meta['name'] in excluded_nlps:
return
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
assert rules_analyzer.is_independent_noun(doc[referred_index]) or \
rules_analyzer.is_potential_anaphor(doc[referred_index])
assert rules_analyzer.is_potential_anaphor(doc[referring_index])
referred_mention = Mention(doc[referred_index], include_dependent_siblings)
if consider_syntax:
self.assertEqual(expected_truth,
rules_analyzer.language_independent_is_potential_anaphoric_pair(
referred_mention, doc[referring_index]), nlp.meta['name'])
else:
self.assertEqual(expected_truth, rules_analyzer.is_potential_anaphoric_pair(
referred_mention, doc[referring_index], False), nlp.meta['name'])
def func(nlp):
if nlp.meta['name'] in excluded_nlps:
return
doc = nlp(doc_text)
rules_analyzer = RulesAnalyzerFactory.get_rules_analyzer(nlp)
rules_analyzer.initialize(doc)
if expected_potential_referreds is None:
self.assertFalse(
hasattr(doc[index]._.coref_chains,
'temp_potential_referreds'))
else:
potential_referreds = [
referred.pretty_representation for referred in
doc[index]._.coref_chains.temp_potential_referreds
]
self.assertEqual(expected_potential_referreds,
potential_referreds, nlp.meta['name'])
def setUp(self):
self.nlps = get_nlps('en')
self.rules_analyzers = [
RulesAnalyzerFactory.get_rules_analyzer(nlp) for nlp in self.nlps
]