def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_quote_array, doc[index]._.coref_chains.temp_quote_array, nlp.meta['name'])
def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_sent_indexes, [token._.coref_chains.temp_sent_index for token in doc], nlp.meta['name'])
def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_sent_starts, doc._.coref_chains.temp_sent_starts, nlp.meta['name'])
def setUp(self): self.nlps = get_nlps('en') self.rules_analyzers = [RulesAnalyzerFactory().get_rules_analyzer(nlp) for nlp in self.nlps] self.sm_nlp = [nlp for nlp in self.nlps if nlp.meta['name'] == 'core_web_sm'][0] self.sm_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(self.sm_nlp)
def setUp(self): warnings.filterwarnings("ignore", message=r"\[W007\]", category=UserWarning) nlps = get_nlps('en') for nlp in (nlp for nlp in nlps if nlp.meta['name'] == 'core_web_sm'): self.sm_nlp = nlp self.sm_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer( self.sm_nlp) sm_model_generator = ModelGenerator(self.sm_rules_analyzer, self.sm_nlp, self.sm_nlp) sm_doc = self.sm_nlp('Richard said he was entering the big house') self.sm_feature_table = sm_model_generator.generate_feature_table( [sm_doc]) self.sm_tendencies_analyzer = TendenciesAnalyzer( self.sm_rules_analyzer, self.sm_nlp, self.sm_feature_table) for nlp in (nlp for nlp in nlps if nlp.meta['name'] == 'core_web_lg'): self.lg_nlp = nlp self.lg_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer( self.lg_nlp) lg_model_generator = ModelGenerator(self.lg_rules_analyzer, self.lg_nlp, self.lg_nlp) lg_doc = self.lg_nlp('Richard said he was entering the big house') self.lg_feature_table = lg_model_generator.generate_feature_table( [lg_doc]) self.lg_tendencies_analyzer = TendenciesAnalyzer( self.lg_rules_analyzer, self.lg_nlp, self.lg_feature_table)
def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) non_or_truths = [token.i for token in doc if rules_analyzer.is_involved_in_non_or_conjunction(token)] self.assertEqual(expected_trues, non_or_truths, 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_potential_coreferring_noun_pair(doc[referred_index], doc[referring_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) 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 setUp(self): for nlp in (nlp for nlp in get_nlps('en') if nlp.meta['name'] == 'core_web_sm'): self.sm_nlp = nlp self.sm_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer( nlp)
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): 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_introducing_noun(doc[index]), 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): doc = nlp('they') rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(True, rules_analyzer.has_morph(doc[0], 'Number'), nlp.meta['name']) self.assertEqual(False, rules_analyzer.has_morph(doc[0], 'Other'), 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) 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 ]
class CommonRulesTest(unittest.TestCase): def setUp(self): self.nlps = get_nlps('en') self.rules_analyzers = [RulesAnalyzerFactory().get_rules_analyzer(nlp) for nlp in self.nlps] self.sm_nlp = [nlp for nlp in self.nlps if nlp.meta['name'] == 'core_web_sm'][0] self.sm_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(self.sm_nlp) def all_nlps(self, func): for nlp in self.nlps: func(nlp) def compare_sent_starts(self, doc_text, expected_sent_starts): def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_sent_starts, doc._.coref_chains.temp_sent_starts, nlp.meta['name']) self.all_nlps(func) def test_sent_starts(self): self.compare_sent_starts('My name is Charles. I am here. The weather is good', [0, 5, 9]) def compare_sent_indexes(self, doc_text, expected_sent_indexes): def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_sent_indexes, [token._.coref_chains.temp_sent_index for token in doc], nlp.meta['name']) self.all_nlps(func) def test_sent_index(self): self.compare_sent_indexes('My name is Charles. I am here. The weather is good', [0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]) def compare_get_dependent_sibling_info(self, doc_text, index, expected_dependent_siblings, expected_governing_sibling, expected_has_or_coordination): 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']) self.all_nlps(func) def test_get_dependent_sibling_info_no_conjunction(self): self.compare_get_dependent_sibling_info('Richard went home', 0, '[]', None, False) def test_get_dependent_sibling_info_two_member_conjunction_phrase_and(self): self.compare_get_dependent_sibling_info('Richard and Christine went home', 0, '[Christine]', None, False) def test_get_dependent_sibling_info_two_member_conjunction_phrase_or(self): self.compare_get_dependent_sibling_info('Richard or Christine went home', 0, '[Christine]', None, True) def test_get_dependent_sibling_info_three_member_conjunction_phrase_with_comma_and(self): self.compare_get_dependent_sibling_info('Carol, Richard and Ralf had a meeting', 0, '[Richard, Ralf]', None, False) def test_get_dependent_sibling_info_three_member_conjunction_phrase_with_comma_or(self): self.compare_get_dependent_sibling_info('Carol, Richard or Ralf had a meeting', 0, '[Richard, Ralf]', None, True) def test_get_dependent_sibling_info_three_member_conjunction_phrase_with_and(self): self.compare_get_dependent_sibling_info( 'There was a meeting with Carol and Ralf and Richard', 5, '[Ralf, Richard]', None, False) def test_get_dependent_sibling_info_three_member_conjunction_phrase_with_or(self): self.compare_get_dependent_sibling_info( 'A meeting with Carol or Ralf or Richard took place', 3, '[Ralf, Richard]', None, True) def test_get_dependent_sibling_info_three_member_conjunction_phrase_with_and_and_or(self): self.compare_get_dependent_sibling_info( 'There was a meeting with Carol or Ralf and Richard', 5, '[Ralf, Richard]', None, True) def test_get_dependent_sibling_info_conjunction_itself(self): self.compare_get_dependent_sibling_info( 'There was a meeting with Carol and Ralf and Richard', 6, '[]', None, False) def test_get_dependent_sibling_info_dependent_sibling(self): self.compare_get_dependent_sibling_info( 'There was a meeting with Carol and Ralf and Richard', 7, '[]', 5, False) def compare_quote_array(self, doc_text, index, expected_quote_array): def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(expected_quote_array, doc[index]._.coref_chains.temp_quote_array, nlp.meta['name']) self.all_nlps(func) def test_quote_array_simple(self): self.compare_quote_array("He said 'Give it back'", 1, [0, 0, 0, 0]) self.compare_quote_array("He said 'Give it back'", 3, [1, 0, 0, 0]) def test_quote_array_complex(self): self.compare_quote_array("He said “Give it 'back'”", 1, [0, 0, 0, 0]) self.compare_quote_array("He said “Give it 'back'”", 3, [0, 0, 1, 0]) self.compare_quote_array("He said “Give it 'back'”", 6, [1, 0, 1, 0]) def compare_potential_noun_pair(self, doc_text, referred_index, referring_index, expected_truth, *, excluded_nlps=[]): 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_potential_coreferring_noun_pair(doc[referred_index], doc[referring_index]), nlp.meta['name']) self.all_nlps(func) def test_potential_noun_pair_proper_noun_referred(self): self.compare_potential_noun_pair('This is Peter. Peter is here', 2, 4, True) def test_potential_noun_pair_proper_noun_referred_multiword(self): self.compare_potential_noun_pair('This is Peter Smith. Peter Smith is here', 3, 6, True) def test_potential_noun_pair_proper_noun_referred_multiword_referring_end(self): self.compare_potential_noun_pair('This is Peter Smith. Smith is here', 3, 5, True) def test_potential_noun_pair_proper_noun_referred_multiword_referring_beginning(self): self.compare_potential_noun_pair('This is Peter Smith. Peter is here', 3, 5, False) def test_potential_noun_pair_referred_proper_noun_with_child(self): self.compare_potential_noun_pair('I spoke to big Peter. Peter is here', 4, 6, True) def test_potential_noun_pair_referred_proper_noun_conjunction_first_member(self): self.compare_potential_noun_pair('I spoke to Peter and Jane. Peter is here', 3, 7, True) def test_potential_noun_pair_referred_proper_noun_conjunction_second_member(self): self.compare_potential_noun_pair('I spoke to Peter and Jane. Jane is here', 5, 7, True) def test_potential_noun_pair_referring_back_proper_noun_with_non_proper_noun_child(self): self.compare_potential_noun_pair('I spoke to Peter. Big Peter is here', 3, 6, True, excluded_nlps=['core_web_sm', 'core_web_trf']) def test_potential_noun_pair_referring_back_proper_noun_with_proper_noun_child(self): self.compare_potential_noun_pair('I spoke to Peter. Big Peter is here', 3, 6, False, excluded_nlps=['core_web_md', 'core_web_lg']) def test_potential_noun_pair_referring_back_proper_noun_conjunction_first_member(self): self.compare_potential_noun_pair('I spoke to Peter. Peter and Jane are here', 3, 5, True) def test_potential_noun_pair_referring_back_proper_noun_conjunction_second_member(self): self.compare_potential_noun_pair('I spoke to Jane. Peter and Jane are here', 3, 7, True) def test_potential_noun_pair_definite_common_noun_referred(self): self.compare_potential_noun_pair('This is a man. The man is here', 3, 6, True) def test_potential_noun_pair_indefinite_common_noun_referred(self): self.compare_potential_noun_pair('This is the man. A man is here', 3, 6, False) def test_potential_noun_pair_definite_plural_common_noun_referred_1(self): self.compare_potential_noun_pair('These are men. The men is here', 2, 5, False) def test_potential_noun_pair_definite_plural_common_noun_referred_2(self): self.compare_potential_noun_pair('These are some men. The men are here', 3, 6, True) def test_potential_noun_pair_definite_plural_common_noun_referred_3(self): self.compare_potential_noun_pair('These are the men. The men are here', 3, 6, True) def test_potential_noun_pair_indefinite_plural_common_noun_referred(self): self.compare_potential_noun_pair('These are men. Men is here', 2, 4, False) def test_potential_noun_pair_numbers_do_not_match(self): self.compare_potential_noun_pair('This is a man. The men are here.', 3, 6, False) def test_potential_noun_pair_entity_labels_referred(self): self.compare_potential_noun_pair( 'I spoke to the boss of Lehman Brothers. The company went bust', 7, 10, True) def test_potential_noun_pair_entity_labels_referred_not_definite_control(self): self.compare_potential_noun_pair( 'I spoke to the boss of Lehman Brothers. A company went bust', 7, 10, False) def test_potential_noun_pair_wrong_entity_label_referred(self): self.compare_potential_noun_pair( 'I spoke to the boss of Lehman Brothers. The person went bust', 7, 10, False) def test_potentially_independent_nouns_stored_on_token(self): doc = self.sm_nlp('They went to look at the space suits') self.sm_rules_analyzer.initialize(doc) self.assertFalse(doc[3]._.coref_chains.temp_potentially_referring) self.assertFalse(doc[6]._.coref_chains.temp_potentially_referring) self.assertTrue(doc[7]._.coref_chains.temp_potentially_referring) def compare_potential_pair(self, doc_text, referred_index, include_dependent_siblings, referring_index, expected_truth, consider_syntax=True, *, excluded_nlps=[]): 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']) self.all_nlps(func) def test_closer_within_structure_propn(self): self.compare_potential_pair('Richard arrived. Richard saw him.', 0, False, 5, 1) def test_closer_within_structure_propn_conjunction_first(self): self.compare_potential_pair('Richard and Peter arrived. Richard saw him.', 0, False, 7, 1) def test_closer_within_structure_propn_conjunction_second(self): self.compare_potential_pair('Richard and Peter arrived. Peter saw him.', 2, False, 7, 1) def test_closer_within_structure_child(self): self.compare_potential_pair('The dog arrived. The big dog saw him.', 1, False, 8, 2) def test_closer_within_structure_only_determiner(self): self.compare_potential_pair('The dog arrived. The dog saw him.', 1, False, 7, 1) def test_closer_within_structure_only_determiner_conjunction_first(self): self.compare_potential_pair('The dog and the cat arrived. The dog saw him.', 1, False, 10, 1) def test_closer_within_structure_only_determiner_conjunction_second(self): self.compare_potential_pair('The dog and the cat arrived. The cat saw him.', 4, False, 10, 1) def test_closer_within_structure_all_pronouns_control(self): self.compare_potential_pair('He arrived. He saw him.', 0, False, 5, 2) def test_consider_syntax_false(self): self.compare_potential_pair('He saw him.', 0, False, 2, 2, False) def test_consider_syntax_false_control(self): self.compare_potential_pair('He saw him', 0, False, 2, 0, True) def test_quotes(self): self.compare_potential_pair('"Richard is here", he said.', 1, False, 6, 1) def test_propn_subtree_beginning(self): doc = self.sm_nlp('Richard Hudson is here') self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[0])]) self.assertEqual([0, 1], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[1])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[3])]) def test_propn_subtree_middle(self): doc = self.sm_nlp('He spoke to Richard Hudson yesterday') self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[0])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[3])]) self.assertEqual([3, 4], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[4])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[5])]) def test_propn_subtree_end(self): doc = self.sm_nlp('He spoke to Richard Hudson') self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[0])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[3])]) self.assertEqual([3, 4], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[4])]) def test_propn_subtree_with_coordination(self): doc = self.sm_nlp('Richard Hudson and Peter Jones are here') self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[0])]) self.assertEqual([0, 1], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[1])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[2])]) self.assertEqual([], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[3])]) self.assertEqual([3, 4], [t.i for t in self.sm_rules_analyzer.get_propn_subtree(doc[4])]) def compare_potentially_referring(self, doc_text, expected_per_indexes, *, excluded_nlps=[]): 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']) self.all_nlps(func) def test_has_morph_without_value(self): def func(nlp): doc = nlp('they') rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(True, rules_analyzer.has_morph(doc[0], 'Number'), nlp.meta['name']) self.assertEqual(False, rules_analyzer.has_morph(doc[0], 'Other'), nlp.meta['name']) self.all_nlps(func) def test_has_morph_with_value(self): def func(nlp): doc = nlp('they') rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) self.assertEqual(True, rules_analyzer.has_morph( doc[0], 'Number', 'Plur'), nlp.meta['name']) self.assertEqual(False, rules_analyzer.has_morph( doc[0], 'Number', 'Other'), nlp.meta['name']) self.assertEqual(False, rules_analyzer.has_morph( doc[0], 'Other', 'Other'), nlp.meta['name']) self.all_nlps(func) def compare_non_or_truths(self, doc_text, expected_trues): def func(nlp): doc = nlp(doc_text) rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer(nlp) rules_analyzer.initialize(doc) non_or_truths = [token.i for token in doc if rules_analyzer.is_involved_in_non_or_conjunction(token)] self.assertEqual(expected_trues, non_or_truths, nlp.meta['name']) self.all_nlps(func) def test_is_involved_in_non_or_conjunction_and_conjunction(self): self.compare_non_or_truths('Richard and Christine went home', [0,2]) def test_is_involved_in_non_or_conjunction_or_conjunction(self): self.compare_non_or_truths('Richard or Christine went home', []) def test_is_involved_in_non_or_conjunction_mixed_conjunction(self): self.compare_non_or_truths('Richard or Christine and Peter went home', []) def compare_potentially_introducing(self, doc_text, index, expected_truth, *, excluded_nlps=[]): 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_introducing_noun(doc[index]), nlp.meta['name']) self.all_nlps(func) def test_potentially_introducing_not_noun(self): self.compare_potentially_introducing('I spoke to Peter', 2, False) def test_potentially_introducing_definite_noun(self): self.compare_potentially_introducing('I spoke to the man', 4, False) def test_potentially_introducing_indefinite_noun(self): self.compare_potentially_introducing('I spoke to a man', 4, True) def test_potentially_introducing_definite_noun_with_adjective(self): self.compare_potentially_introducing('I spoke to the big man', 5, True) def test_potentially_introducing_definite_noun_with_dependent_phrase(self): self.compare_potentially_introducing('I saw the man whom we had discussed', 3, True, excluded_nlps='core_web_sm') def test_potentially_introducing_common_noun_conjunction_first_member(self): self.compare_potentially_introducing('I spoke to a man and a woman', 4, True) def test_potentially_introducing_common_noun_conjunction_second_member(self): self.compare_potentially_introducing('I spoke to a man and a woman', 7, True) def compare_potentially_referring_back_noun(self, doc_text, index, expected_truth, *, excluded_nlps=[]): 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']) self.all_nlps(func) def test_potentially_referring_back_noun_not_noun(self): self.compare_potentially_referring_back_noun('I spoke to Peter', 2, False) def test_potentially_referring_back_noun_definite_noun(self): self.compare_potentially_referring_back_noun('I spoke to the man', 4, True) def test_potentially_referring_back_noun_indefinite_noun(self): self.compare_potentially_referring_back_noun('I spoke to a man', 4, False) def test_potentially_referring_back_noun_definite_noun_with_adjective(self): self.compare_potentially_referring_back_noun('I spoke to the big man', 5, False) def test_potentially_referring_back_noun_definite_noun_with_dependent_phrase(self): self.compare_potentially_referring_back_noun('I saw the man whom we had discussed', 3, False, excluded_nlps='core_web_sm') def test_potentially_referring_back_noun_common_noun_conjunction_first_member(self): self.compare_potentially_referring_back_noun('I spoke to the man and the woman', 4, True) def test_potentially_referring_back_noun_common_noun_conjunction_second_member(self): self.compare_potentially_referring_back_noun('I spoke to the man and the woman', 7, True)
class CommonTendenciesTest(unittest.TestCase): def setUp(self): warnings.filterwarnings("ignore", message=r"\[W007\]", category=UserWarning) nlps = get_nlps('en') for nlp in (nlp for nlp in nlps if nlp.meta['name'] == 'core_web_sm'): self.sm_nlp = nlp self.sm_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer( self.sm_nlp) sm_model_generator = ModelGenerator(self.sm_rules_analyzer, self.sm_nlp, self.sm_nlp) sm_doc = self.sm_nlp('Richard said he was entering the big house') self.sm_feature_table = sm_model_generator.generate_feature_table( [sm_doc]) self.sm_tendencies_analyzer = TendenciesAnalyzer( self.sm_rules_analyzer, self.sm_nlp, self.sm_feature_table) for nlp in (nlp for nlp in nlps if nlp.meta['name'] == 'core_web_lg'): self.lg_nlp = nlp self.lg_rules_analyzer = RulesAnalyzerFactory().get_rules_analyzer( self.lg_nlp) lg_model_generator = ModelGenerator(self.lg_rules_analyzer, self.lg_nlp, self.lg_nlp) lg_doc = self.lg_nlp('Richard said he was entering the big house') self.lg_feature_table = lg_model_generator.generate_feature_table( [lg_doc]) self.lg_tendencies_analyzer = TendenciesAnalyzer( self.lg_rules_analyzer, self.lg_nlp, self.lg_feature_table) def test_generate_feature_table(self): doc = self.sm_nlp('Richard said he was entering the big house') model_generator = ModelGenerator(self.sm_rules_analyzer, self.sm_nlp, self.sm_nlp) feature_table = model_generator.generate_feature_table([doc]) self.assertEqual( { 'tags': ['NN', 'NNP', 'PRP'], 'morphs': [ 'Case=Nom', 'Gender=Masc', 'NounType=Prop', 'Number=Sing', 'Person=3', 'PronType=Prs' ], 'ent_types': ['', 'PERSON'], 'lefthand_deps_to_children': ['amod', 'det'], 'righthand_deps_to_children': [], 'lefthand_deps_to_parents': ['nsubj'], 'righthand_deps_to_parents': ['dobj'], 'parent_tags': ['VBD', 'VBG'], 'parent_morphs': [ 'Aspect=Prog', 'Tense=Past', 'Tense=Pres', 'VerbForm=Fin', 'VerbForm=Part' ], 'parent_lefthand_deps_to_children': ['aux', 'nsubj'], 'parent_righthand_deps_to_children': ['ccomp', 'dobj'] }, feature_table.__dict__) self.assertEqual(26, len(feature_table)) def test_get_feature_map_simple_mention(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) mention = Mention(doc[0], False) feature_map = self.sm_tendencies_analyzer.get_feature_map(mention, doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual(mention.temp_feature_map, feature_map) self.assertEqual([ 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0 ], feature_map) feature_map = self.sm_tendencies_analyzer.get_feature_map( Mention(doc[2], False), doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual([ 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1 ], feature_map) def test_get_feature_map_simple_token(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) feature_map = self.sm_tendencies_analyzer.get_feature_map(doc[0], doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual(doc[0]._.coref_chains.temp_feature_map, feature_map) self.assertEqual([ 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0 ], feature_map) feature_map = self.sm_tendencies_analyzer.get_feature_map(doc[2], doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual([ 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1 ], feature_map) def test_get_feature_map_conjunction(self): doc = self.sm_nlp( 'Richard and the man said they were entering the big house') self.sm_rules_analyzer.initialize(doc) feature_map = self.sm_tendencies_analyzer.get_feature_map( Mention(doc[0], False), doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual([ 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0 ], feature_map) feature_map = self.sm_tendencies_analyzer.get_feature_map( Mention(doc[0], True), doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual([ 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0 ], feature_map) feature_map = self.sm_tendencies_analyzer.get_feature_map( Mention(doc[5], False), doc) self.assertEqual(len(self.sm_feature_table), len(feature_map)) self.assertEqual([ 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1 ], feature_map) def test_get_position_map_first_sentence_token(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( doc[0], doc) self.assertEqual(doc[0]._.coref_chains.temp_position_map, position_map) self.assertEqual([0, 1, 1, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[2], doc) self.assertEqual([2, 2, 2, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[6], doc) self.assertEqual([6, 3, 2, 1, 1, 0, 0], position_map) def test_get_position_map_first_sentence_mention(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) mention = Mention(doc[0], False) position_map = self.sm_tendencies_analyzer.get_position_map( mention, doc) self.assertEqual(mention.temp_position_map, position_map) self.assertEqual([0, 1, 1, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[2], False), doc) self.assertEqual([2, 2, 2, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[6], False), doc) self.assertEqual([6, 3, 2, 1, 1, 0, 0], position_map) def test_get_position_map_second_sentence_token(self): doc = self.sm_nlp( 'This is a preceding sentence. Richard said he was entering the big house' ) self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( doc[6], doc) self.assertEqual([0, 1, 1, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[8], doc) self.assertEqual([2, 2, 2, 0, 0, 0, 0], position_map) def test_get_position_map_second_sentence_mention(self): doc = self.sm_nlp( 'This is a preceding sentence. Richard said he was entering the big house' ) self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[6], False), doc) self.assertEqual([0, 1, 1, 0, 0, 0, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[8], False), doc) self.assertEqual([2, 2, 2, 0, 0, 0, 0], position_map) def test_get_position_map_root_token(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( doc[1], doc) self.assertEqual([1, 0, 0, 0, -1, 0, 0], position_map) def test_get_position_map_root_mention(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[1], False), doc) self.assertEqual([1, 0, 0, 0, -1, 0, 0], position_map) def test_get_position_map_conjunction_first_sentence_tokens(self): doc = self.sm_nlp('Peter and Jane spoke to him and her.') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( doc[0], doc) self.assertEqual([0, 1, 1, 0, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[2], doc) self.assertEqual([2, 2, 1, 1, 1, -1, 1], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[5], doc) self.assertEqual([5, 2, 1, 2, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( doc[7], doc) self.assertEqual([7, 3, 1, 1, 1, -1, 1], position_map) def test_get_position_map_conjunction_first_sentence_mentions_false(self): doc = self.sm_nlp('Peter and Jane spoke to him and her.') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[0], False), doc) self.assertEqual([0, 1, 1, 0, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[2], False), doc) self.assertEqual([2, 2, 1, 1, 1, -1, 1], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[5], False), doc) self.assertEqual([5, 2, 1, 2, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[7], False), doc) self.assertEqual([7, 3, 1, 1, 1, -1, 1], position_map) def test_get_position_map_conjunction_second_sentence_mentions_false(self): doc = self.sm_nlp( 'A preceding sentence. Peter and Jane spoke to him and her.') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[4], False), doc) self.assertEqual([0, 1, 1, 0, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[6], False), doc) self.assertEqual([2, 2, 1, 1, 1, -1, 1], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[9], False), doc) self.assertEqual([5, 2, 1, 2, 0, -1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[11], False), doc) self.assertEqual([7, 3, 1, 1, 1, -1, 1], position_map) def test_get_position_map_conjunction_first_sentence_mentions_true(self): doc = self.sm_nlp('Peter and Jane spoke to him and her.') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[0], True), doc) self.assertEqual([0, 1, 1, 0, 0, 1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[5], True), doc) self.assertEqual([5, 2, 1, 2, 0, 1, 0], position_map) def test_get_position_map_conjunction_second_sentence_mentions_true(self): doc = self.sm_nlp( 'A preceding sentence. Peter and Jane spoke to him and her.') self.sm_rules_analyzer.initialize(doc) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[4], True), doc) self.assertEqual([0, 1, 1, 0, 0, 1, 0], position_map) position_map = self.sm_tendencies_analyzer.get_position_map( Mention(doc[9], True), doc) self.assertEqual([5, 2, 1, 2, 0, 1, 0], position_map) def test_get_compatibility_map_simple(self): doc = self.sm_nlp('Richard said he was entering the big house') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[2, 0, 1, 0.30341318, 3]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[2]))) def test_get_compatibility_map_coordination(self): doc = self.sm_nlp( 'Richard and Jane said he was entering the big house') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 1, 0.23422348, 3]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], True), doc[4]))) def test_get_compatibility_map_different_sentences(self): doc = self.sm_nlp( 'Richard called. He said he was entering the big house') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[3, 1, 0, 0.507851, 6]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[3]))) def test_get_compatibility_map_same_sentence_no_governance(self): doc = self.sm_nlp( 'After Richard arrived, he said he was entering the big house') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 0, 0.045050576, 5]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[4]))) def test_get_compatibility_map_same_sentence_lefthand_sibling_governance( self): doc = self.lg_nlp( 'Richard said Peter and he were entering the big house') self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 1, 0.15999001, 3]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[4]))) def test_get_compatibility_map_same_sentence_lefthand_sibling_no_governance( self): doc = self.sm_nlp( 'After Richard arrived, Peter and he said he was entering the big house' ) self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[5, 0, 0, 0.41453412, 1]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[1], False), doc[6]))) def test_get_cosine_similarity_lg(self): doc = self.lg_nlp( 'After Richard arrived, he said he was entering the big house') self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 0, 0.3336621, 5]', str( self.lg_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[4]))) def test_get_cosine_similarity_lg_no_vector_1(self): doc = self.lg_nlp( 'After Richard arfewfewfrived, he said he was entering the big house' ) self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 0, 0.59521705, 5]', str( self.lg_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[4]))) def test_get_cosine_similarity_lg_no_vector_2(self): doc = self.lg_nlp( 'After Richard arrived, he saifefefwefefd he was entering the big house' ) self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[4, 0, 0, 0.4391515, 2]', str( self.lg_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[4]))) def test_get_cosine_similarity_sm_root_1(self): doc = self.sm_nlp('Richard. He said he was entering the big house') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[2, 1, 0, -1, 1]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[2]))) def test_get_cosine_similarity_sm_root_2(self): doc = self.sm_nlp('Richard arrived. He.') self.sm_rules_analyzer.initialize(doc) self.assertEqual( '[3, 1, 0, -1, 1]', str( self.sm_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[3]))) def test_get_cosine_similarity_lg_root_1(self): doc = self.lg_nlp('Richard. He said he was entering the big house') self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[2, 1, 0, -1, 1]', str( self.lg_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[2]))) def test_get_cosine_similarity_lg_root_2(self): doc = self.lg_nlp('Richard arrived. He.') self.lg_rules_analyzer.initialize(doc) self.assertEqual( '[3, 1, 0, -1, 1]', str( self.lg_tendencies_analyzer.get_compatibility_map( Mention(doc[0], False), doc[3]))) def test_get_vectors_token_with_head_sm(self): doc = self.sm_nlp('He arrived') self.sm_rules_analyzer.initialize(doc) vectors = self.sm_tendencies_analyzer.get_vectors(doc[0], doc) self.assertTrue(vectors[0].any()) self.assertTrue(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) self.assertEqual(vectors, doc[0]._.coref_chains.temp_vectors) def test_get_vectors_token_without_head_sm(self): doc = self.sm_nlp('He arrived') self.sm_rules_analyzer.initialize(doc) vectors = self.sm_tendencies_analyzer.get_vectors(doc[1], doc) self.assertTrue(vectors[0].any()) self.assertFalse(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) self.assertEqual(vectors, doc[1]._.coref_chains.temp_vectors) def test_get_vectors_token_with_head_lg(self): doc = self.lg_nlp('He arrived') self.lg_rules_analyzer.initialize(doc) vectors = self.lg_tendencies_analyzer.get_vectors(doc[0], doc) self.assertTrue(vectors[0].any()) self.assertTrue(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) self.assertEqual(vectors, doc[0]._.coref_chains.temp_vectors) def test_get_vectors_token_without_head_lg(self): doc = self.lg_nlp('He arrived') self.lg_rules_analyzer.initialize(doc) vectors = self.lg_tendencies_analyzer.get_vectors(doc[1], doc) self.assertTrue(vectors[0].any()) self.assertFalse(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) self.assertEqual(vectors, doc[1]._.coref_chains.temp_vectors) def test_get_vectors_mention_with_head_sm(self): doc = self.sm_nlp('He arrived') self.sm_rules_analyzer.initialize(doc) mention = Mention(doc[0], False) vectors = self.sm_tendencies_analyzer.get_vectors(mention, doc) self.assertTrue(vectors[0].any()) self.assertTrue(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) self.assertEqual(vectors, mention.temp_vectors) def test_get_vectors_mention_without_head_sm(self): doc = self.sm_nlp('He arrived') self.sm_rules_analyzer.initialize(doc) vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[1], False), doc) self.assertTrue(vectors[0].any()) self.assertFalse(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) def test_get_vectors_mention_with_head_lg(self): doc = self.lg_nlp('He arrived') self.lg_rules_analyzer.initialize(doc) vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[0], False), doc) self.assertTrue(vectors[0].any()) self.assertTrue(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) def test_get_vectors_mention_without_head_lg(self): doc = self.lg_nlp('He arrived') self.lg_rules_analyzer.initialize(doc) vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[1], False), doc) self.assertTrue(vectors[0].any()) self.assertFalse(vectors[1].any()) self.assertEqual(len(vectors[0]), len(vectors[1])) def test_vectors_twoway_coordination_sm(self): doc = self.sm_nlp('Peter and Jane arrived') self.sm_rules_analyzer.initialize(doc) peter_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[0], False), doc) jane_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[2], False), doc) combined_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[0], True), doc) for index in range(len(peter_vectors[0])): self.assertAlmostEqual( (peter_vectors[0][index] + jane_vectors[0][index]) / 2, combined_vectors[0][index]) def test_vectors_twoway_coordination_lg(self): doc = self.lg_nlp('Peter and Jane arrived') self.lg_rules_analyzer.initialize(doc) peter_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[0], False), doc) jane_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[2], False), doc) combined_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[0], True), doc) for index in range(len(peter_vectors[0])): self.assertAlmostEqual( (peter_vectors[0][index] + jane_vectors[0][index]) / 2, combined_vectors[0][index]) def test_vectors_threeway_coordination_sm(self): doc = self.sm_nlp('Richard, Peter and Jane arrived') self.sm_rules_analyzer.initialize(doc) richard_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[0], False), doc) peter_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[2], False), doc) jane_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[4], False), doc) combined_vectors = self.sm_tendencies_analyzer.get_vectors( Mention(doc[0], True), doc) for index in range(len(peter_vectors[0])): self.assertAlmostEqual( (peter_vectors[0][index] + jane_vectors[0][index] + richard_vectors[0][index]) / 3, combined_vectors[0][index], places=3) def test_vectors_threeway_coordination_lg(self): doc = self.lg_nlp('They spoke to Richard, Peter and Jane.') self.lg_rules_analyzer.initialize(doc) richard_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[3], False), doc) peter_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[5], False), doc) jane_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[7], False), doc) combined_vectors = self.lg_tendencies_analyzer.get_vectors( Mention(doc[3], True), doc) for index in range(len(peter_vectors[0])): self.assertAlmostEqual( (peter_vectors[0][index] + jane_vectors[0][index] + richard_vectors[0][index]) / 3, combined_vectors[0][index], places=3)