def test_train_pre_processing_ex_person_with_polarity():
sentence = 'It is Santorum that is the by far major nonRomney candidate and Newt would appear to be the spoiler .'
amr: AMR = AMR()
amr.roots = ['a']
amr.reentrance_triples = []
amr.node_to_concepts = {'b': 'by-far', 'm': 'major-02', 'n2': 'name', 'p3': 'person', 'n': 'name',
'p2': 'person', 'c': 'candidate', 'n3': 'name', 'p4': 'person', 's': 'spoil-01',
'a2': 'appear-02', 'a': 'and'}
amr.node_to_tokens = {'b': ['6', '7'], 'Santorum': [('2', 'n')], 'm': ['8'], 'Newt': [('12', 'n3')],
'c': ['10'], 'a2': ['14'], 'a': ['11']}
amr.relation_to_tokens = {'domain': [('1', 'c'), ('4', 'c')]}
amr['b'] = {}
amr['m'] = {'degree': [('b',)]}
amr['Romney'] = {}
amr['n2'] = {'op1': [('Romney',)]}
amr['-'] = {}
amr['p3'] = {'polarity': [('-',)], 'wiki': [('Mitt_Romney',)], 'name': [('n2',)]}
amr['Mitt_Romney'] = {}
amr['Santorum'] = {}
amr['n'] = {'op1': [('Santorum',)]}
amr['Rick_Santorum'] = {}
amr['p2'] = {'wiki': [('Rick_Santorum',)], 'name': [('n',)]}
amr['c'] = {'ARG1-of': [('m',)], 'mod': [('p3',)], 'domain': [('p2',)]}
amr['Newt'] = {}
amr['n3'] = {'op1': [('Newt',)]}
amr['Newt_Gingrich'] = {}
amr['p4'] = {'wiki': [('Newt_Gingrich',)], 'name': [('n3',)]}
amr['s'] = {'ARG0': [('p4',)]}
amr['a2'] = {'ARG1': [('s',)]}
amr['a'] = {'op1': [('c',)], 'op2': [('a2',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# expected
expected_sentence = 'It is PERSON that is the by far major nonRomney candidate and PERSON would appear to be the spoiler .'
expected_metadata = {2: ['Santorum'], 12: ['Newt']}
expected_amr: AMR = AMR()
expected_amr.roots = ['a']
expected_amr.reentrance_triples = []
expected_amr.node_to_concepts = {'b': 'by-far', 'm': 'major-02', 'n2': 'name', 'p3': 'person',
'p2': 'PERSON', 'c': 'candidate', 'p4': 'PERSON', 's': 'spoil-01',
'a2': 'appear-02', 'a': 'and'}
expected_amr.node_to_tokens = {'b': ['6', '7'], 'p2': ['2'], 'm': ['8'], 'p4': ['12'],
'c': ['10'], 'a2': ['14'], 'a': ['11']}
expected_amr.relation_to_tokens = {'domain': [('1', 'c'), ('4', 'c')]}
expected_amr['b'] = {}
expected_amr['m'] = {'degree': [('b',)]}
expected_amr['Romney'] = {}
expected_amr['n2'] = {'op1': [('Romney',)]}
expected_amr['-'] = {}
expected_amr['p3'] = {'polarity': [('-',)], 'wiki': [('Mitt_Romney',)], 'name': [('n2',)]}
expected_amr['Mitt_Romney'] = {}
expected_amr['p2'] = {}
expected_amr['c'] = {'ARG1-of': [('m',)], 'mod': [('p3',)], 'domain': [('p2',)]}
expected_amr['p4'] = {}
expected_amr['s'] = {'ARG0': [('p4',)]}
expected_amr['a2'] = {'ARG1': [('s',)]}
expected_amr['a'] = {'op1': [('c',)], 'op2': [('a2',)]}
assert expected_sentence == generated_sentence
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_metadata == expected_metadata
def test_pre_and_post_processing_eg_2():
sentence = 'It is Santorum that is the by far major nonRomney candidate and Newt would appear to be the spoiler .'
amr_str = """(a / and~e.11
:op1 (c / candidate~e.10
:ARG1-of (m / major-02~e.8
:degree (b / by-far~e.6,7))
:mod (p3 / person~e.9 :polarity -~e.9 :wiki "Mitt_Romney"~e.9
:name (n2 / name~e.9 :op1 "Romney"~e.9))
:domain~e.1,4 (p2 / person :wiki "Rick_Santorum"
:name (n / name :op1 "Santorum"~e.2)))
:op2 (a2 / appear-02~e.14
:ARG1 (s / spoil-01~e.18
:ARG0 (p4 / person :wiki "Newt_Gingrich"
:name (n3 / name :op1 "Newt"~e.12)))))"""
amr: AMR = AMR.parse_string(amr_str)
amr, new_sentence, metadata = train_pre_processing(amr, sentence)
identified_concepts = IdentifiedConcepts()
identified_concepts.create_from_amr('', amr)
add_false_root(identified_concepts)
vector_of_parents = generate_parent_list_vector(amr, identified_concepts)
post_processing_on_parent_vector(identified_concepts, vector_of_parents,
new_sentence, metadata)
relations_dict = {
('and', 'candidate'): 'op1',
('and', 'appear-02'): 'op2',
('candidate', 'major-02'): 'ARG1-of',
('candidate', 'person'): 'mod',
('major-02', 'by-far'): 'degree',
('person', '-'): 'polarity',
('person', 'Mitt_Romney'): 'wiki',
('person', 'name'): 'name',
('person', 'Santorum'): 'wiki',
('name', 'Romney'): 'op1',
('name', 'Santorum'): 'op1',
('appear-02', 'spoil-01'): 'ARG1',
('spoil-01', 'person'): 'ARG0',
('person', 'Newt'): 'wiki',
('name', 'Newt'): 'op1'
}
amr_node: Node = generate_amr_node_for_vector_of_parents(
identified_concepts, vector_of_parents, relations_dict)
generated_amr_str = amr_node.amr_print_with_reentrancy()
expected_amr_str = """(a / and~e.11
:op1 (c / candidate~e.10
:ARG1-of (m / major-02~e.8
:degree (b / by-far~e.6,7))
:mod (p3 / person~e.9 :polarity -~e.9 :wiki "Mitt_Romney"~e.9
:name (n2 / name~e.9 :op1 "Romney"~e.9))
:mod~e.1,4 (p2 / person :wiki "Santorum"
:name (n / name :op1 "Santorum"~e.2)))
:op2 (a2 / appear-02~e.14
:ARG1 (s / spoil-01~e.18
:ARG0 (p4 / person :wiki "Newt"
:name (n3 / name :op1 "Newt"~e.12)))))"""
smatch = calculate_smatch(generated_amr_str, expected_amr_str)
assert smatch == 1
def test_train_pre_processing_ex_organization():
sentence = "Some propaganda activities of ZF have soon become viewed as jokes by the people ."
amr: AMR = AMR()
amr.roots = ['b']
amr.reentrance_triples = [('b', 'ARG1', 'a')]
amr.node_to_concepts = {'p2': 'person', 'n': 'name', 'o': 'organization', 'p': 'propaganda',
's': 'some', 'a': 'activity-06', 'j': 'joke-01', 't': 'thing',
'v': 'view-02', 's2': 'soon', 'b': 'become-01'}
amr.node_to_tokens = {'ZF': [('4', 'n')], 'p': ['1'], 's': ['0'], 'j': ['10'],
'p2': ['13'], 'a': ['2'], 't': ['10'], 'v': ['8'], 's2': ['6'], 'b': ['7']}
amr.relation_to_tokens = {'ARG0': [('3', 'a'), ('11', 'v')], 'ARG2-of': [('10', 't')], 'ARG2': [('9', 'v')],
'time': [('9', 'b')]}
amr['p2'] = {}
amr['ZF'] = {}
amr['n'] = {'op1': [('ZF',)]}
amr['-'] = {}
amr['o'] = {'wiki': [('-',)], 'name': [('n',)]}
amr['p'] = {}
amr['s'] = {}
amr['a'] = {'ARG0': [('o',)], 'ARG1': [('p',)], 'quant': [('s',)]}
amr['j'] = {}
amr['t'] = {'ARG2-of': [('j',)]}
amr['v'] = {'ARG0': [('p2',)], 'ARG1': [('a',)], 'ARG2': [('t',)]}
amr['s2'] = {}
amr['b'] = {'ARG1': [('a',)], 'ARG2': [('v',)], 'time': [('s2',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# Expected
expected_sentence = "Some propaganda activities of ORGANIZATION have soon become viewed as jokes by the people ."
expected_metadata = {4: ['ZF']}
expected_amr: AMR = AMR()
expected_amr.roots = ['b']
expected_amr.reentrance_triples = [('b', 'ARG1', 'a')]
expected_amr.node_to_concepts = {'p2': 'person', 'o': 'ORGANIZATION', 'p': 'propaganda',
's': 'some', 'a': 'activity-06', 'j': 'joke-01', 't': 'thing',
'v': 'view-02', 's2': 'soon', 'b': 'become-01'}
expected_amr.node_to_tokens = {'o': ['4'], 'p': ['1'], 's': ['0'], 'j': ['10'],
'p2': ['13'], 'a': ['2'], 't': ['10'], 'v': ['8'], 's2': ['6'], 'b': ['7']}
expected_amr.relation_to_tokens = {'ARG0': [('3', 'a'), ('11', 'v')], 'ARG2-of': [('10', 't')],
'ARG2': [('9', 'v')],
'time': [('9', 'b')]}
expected_amr['p2'] = {}
expected_amr['o'] = {}
expected_amr['p'] = {}
expected_amr['s'] = {}
expected_amr['a'] = {'ARG0': [('o',)], 'ARG1': [('p',)], 'quant': [('s',)]}
expected_amr['j'] = {}
expected_amr['t'] = {'ARG2-of': [('j',)]}
expected_amr['v'] = {'ARG0': [('p2',)], 'ARG1': [('a',)], 'ARG2': [('t',)]}
expected_amr['s2'] = {}
expected_amr['b'] = {'ARG1': [('a',)], 'ARG2': [('v',)], 'time': [('s2',)]}
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_sentence == expected_sentence
assert generated_metadata == expected_metadata
def test_train_pre_processing_ex_person_reentrancy():
sentence = 'Now , Wang Shi said , these responses have had effects on me .'
amr: AMR = AMR()
amr.roots = ['s']
amr.reentrance_triples = [('e', 'ARG1', 'p')]
amr.node_to_concepts = {'n2': 'name', 'p': 'person', 'r': 'respond-01', 't': 'this',
't2': 'thing', 'e': 'effect-03', 'n': 'now', 's': 'say-01'}
amr.node_to_tokens = {'Wang': [('2', 'n2')], 'Shi': [('3', 'n2')], 'r': ['7'], 't': ['6'],
't2': ['7'], 'p': ['12'], 'e': ['10'], 'n': ['0'], 's': ['4']}
amr.relation_to_tokens = {'ARG2-of': [('7', 't2')], 'ARG1': [('11', 'e')]}
amr['Wang'] = {}
amr['n2'] = {'op1': [('Wang',)], 'op2': [('Shi',)]}
amr['Shi'] = {}
amr['Wang_Shi_(entrepreneur)'] = {}
amr['p'] = {'wiki': [('Wang_Shi_(entrepreneur)',)], 'name': [('n2',)]}
amr['r'] = {}
amr['t'] = {}
amr['t2'] = {'ARG2-of': [('r',)], 'mod': [('t',)]}
amr['e'] = {'ARG0': [('t2',)], 'ARG1': [('p',)]}
amr['n'] = {}
amr['s'] = {'ARG0': [('p',)], 'ARG1': [('e',)], 'time': [('n',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# expected
expected_sentence = 'Now , PERSON said , these responses have had effects on me .'
expected_metadata = {2: ['Wang', 'Shi']}
expected_amr: AMR = AMR()
expected_amr.roots = ['s']
expected_amr.reentrance_triples = [('e', 'ARG1', 'p')]
expected_amr.node_to_concepts = {'p': 'PERSON', 'r': 'respond-01', 't': 'this',
't2': 'thing', 'e': 'effect-03', 'n': 'now', 's': 'say-01'}
expected_amr.node_to_tokens = {'p': ['2'], 'r': ['6'], 't': ['5'],
't2': ['6'], 'p': ['11'], 'e': ['9'], 'n': ['0'], 's': ['3']}
expected_amr.relation_to_tokens = {'ARG2-of': [('6', 't2')], 'ARG1': [('10', 'e')]}
expected_amr['p'] = {}
expected_amr['r'] = {}
expected_amr['t'] = {}
expected_amr['t2'] = {'ARG2-of': [('r',)], 'mod': [('t',)]}
expected_amr['e'] = {'ARG0': [('t2',)], 'ARG1': [('p',)]}
expected_amr['n'] = {}
expected_amr['s'] = {'ARG0': [('p',)], 'ARG1': [('e',)], 'time': [('n',)]}
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_sentence == expected_sentence
assert generated_metadata == expected_metadata
def test_pre_and_post_processing_for_organization():
sentence = 'Some propaganda activities of ZF have soon become viewed as jokes by the people .'
amr_str = """(b / become-01~e.7
:ARG1 a
:ARG2 (v / view-02~e.8
:ARG0~e.11 (p2 / person~e.13)
:ARG1 (a / activity-06~e.2
:ARG0~e.3 (o / organization :wiki ZF
:name (n / name :op1 "ZF"~e.4))
:ARG1 (p / propaganda~e.1)
:quant (s / some~e.0))
:ARG2~e.9 (t / thing~e.10
:ARG2-of~e.10 (j / joke-01~e.10)))
:time~e.9 (s2 / soon~e.6))"""
amr: AMR = AMR.parse_string(amr_str)
amr, new_sentence, metadata = train_pre_processing(amr, sentence)
identified_concepts = IdentifiedConcepts()
identified_concepts.create_from_amr('', amr)
add_false_root(identified_concepts)
vector_of_parents = generate_parent_list_vector(amr, identified_concepts)
post_processing_on_parent_vector(identified_concepts, vector_of_parents,
new_sentence, metadata)
relations_dict = {
('become-01', 'activity-06'): 'ARG1',
('become-01', 'view-02'): 'ARG2',
('become-01', 'soon'): 'time',
('view-02', 'person'): 'ARG0',
('view-02', 'activity-06'): 'ARG1',
('view-02', 'thing'): 'ARG2',
('activity-06', 'organization'): 'ARG0',
('activity-06', 'propaganda'): 'ARG1',
('activity-06', 'some'): 'quant',
('organization', 'ZF'): 'wiki',
('organization', 'name'): 'name',
('name', 'ZF'): 'op1',
('thing', 'joke-01'): 'ARG2-of'
}
amr_node: Node = generate_amr_node_for_vector_of_parents(
identified_concepts, vector_of_parents, relations_dict)
generated_amr_str = amr_node.amr_print_with_reentrancy()
smatch = calculate_smatch(generated_amr_str, amr_str)
assert smatch == 1
def train_pre_processing_ex_persons_with_common_tokens():
sentence = 'On the day of the Tangshan Earthquake , i.e. July 28 th , those on duty at Mao Zedong \'s quarters ' \
'were Wang Dongxing , Wang Hongwen , and Mao Zedong \'s confidential secretary , Zhang Yufeng . '
amr: AMR = AMR()
amr.roots = ['p']
amr.reentrance_triples = [('q', 'poss', 'p5')]
amr.node_to_concepts = {'n5': 'name', 'e': 'earthquake', 'd3': 'date-entity', 'm': 'mean-01',
'd2': 'day', 'q': 'quarter', 'd': 'duty', 'n': 'name', 'p2': 'person',
'n2': 'name', 'p3': 'person', 'n3': 'name', 'n4': 'name', 'p5': 'person',
'c': 'confidence', 's': 'secretary', 'h': 'have-org-role-91', 'p4': 'person',
'a': 'and', 'p': 'person'}
amr.node_to_tokens = {'Tangshan': [('5', 'n5')], 'Earthquake': [('6', 'n5')], '7': [('9', 'd3')],
'28': [('10', 'd3')], 'p5': ['17', '18'], 'd2': ['2'], 'q': ['20'],
'Wang': [('22', 'n'), ('25', 'n2')], 'Dongxing': [('23', 'n')],
'Hongwen': [('26', 'n2')], 'Zhang': [('35', 'n3')],
'Yufeng': [('36', 'n3')], 'Mao': [('29', 'n4')], 'Zedong': [('30', 'n4')],
's': ['33'], 'h': ['31'], 'd': ['15'], 'a': ['28']}
amr.relation_to_tokens = {'month': [('9', 'd3')], 'time-of': [('3', 'd2')], 'poss': [('19', 'q')],
'location': [('16', 'd')], 'prep-on': [('0', 'p'), ('14', 'p')],
'domain': [('8', 'p'), ('19', 'p'), ('21', 'p')]}
amr['Tangshan'] = {}
amr['n5'] = {'op1': [('Tangshan',)], 'op2': [('Earthquake',)]}
amr['Earthquake'] = {}
amr['1976_Tangshan_earthquake'] = {}
amr['e'] = {'wiki': [('1976_Tangshan_earthquake',)], 'name': [('n5',)]}
amr['7'] = {}
amr['d3'] = {'month': [('7',)], 'day': [('28',)]}
amr['28'] = {}
amr['m'] = {'ARG2': [('d3',)]}
amr['d2'] = {'time-of': [('e',)], 'ARG1-of': [('m',)]}
amr['p5'] = {'wiki': [('Mao_Zedong',)], 'name': [('n4',)]}
amr['q'] = {'poss': [('p5',)]}
amr['d'] = {'time': [('d2',)], 'location': [('q',)]}
amr['Wang'] = {}
amr['n'] = {'op1': [('Wang',)], 'op2': [('Dongxing',)]}
amr['Dongxing'] = {}
amr['Wang_Dongxing'] = {}
amr['p2'] = {'wiki': [('Wang_Dongxing',)], 'name': [('n',)]}
amr['n2'] = {'op1': [('Wang',)], 'op2': [('Hongwen',)]}
amr['Hongwen'] = {}
amr['Wang_Hongwen'] = {}
amr['p3'] = {'wiki': [('Wang_Hongwen',)], 'name': [('n2',)]}
amr['Zhang'] = {}
amr['n3'] = {'op1': [('Zhang',)], 'op2': [('Yufeng',)]}
amr['Yufeng'] = {}
amr['Mao'] = {}
amr['n4'] = {'op1': [('Mao',)], 'op2': [('Zedong',)]}
amr['Zedong'] = {}
amr['Mao_Zedong'] = {}
amr['c'] = {}
amr['s'] = {'mod': [('c',)]}
amr['h'] = {'ARG1': [('p5',)], 'ARG2': [('s',)]}
amr['-'] = {}
amr['p4'] = {'wiki': [('-',)], 'name': [('n3',)], 'ARG0-of': [('h',)]}
amr['a'] = {'op1': [('p2',)], 'op2': [('p3',)], 'op3': [('p4',)]}
amr['p'] = {'prep-on': [('d',)], 'domain': [('a',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# expected
# maybe I should replace it with PERSON1, PERSON2 or smth, I am losing a lot of info like this
expected_sentence = 'On the day of the Tangshan Earthquake , i.e. July 28 th , those on duty at PERSON \'s ' \
'quarters ' \
'were PERSON , PERSON , and PERSON \'s confidential secretary , PERSON .'
expected_metadata = {17: ['Mao', 'Zedong'], 21: ['Wang', 'Dongxing'], 23: ['Wang', 'Hongwen'],
26: ['Mao', 'Zedong'], 31: ['Zhang', 'Yufeng']}
expected_amr: AMR = AMR()
expected_amr.roots = ['p']
expected_amr.reentrance_triples = [('q', 'poss', 'p5')]
expected_amr.node_to_concepts = {'n5': 'name', 'e': 'earthquake', 'd3': 'date-entity', 'm': 'mean-01',
'd2': 'day', 'q': 'quarter', 'd': 'duty', 'p2': 'PERSON',
'p3': 'PERSON', 'p5': 'PERSON',
'c': 'confidence', 's': 'secretary', 'h': 'have-org-role-91', 'p4': 'PERSON',
'a': 'and', 'p': 'person'}
# todo: maybe make sure the values in the alignment list are unique
expected_amr.node_to_tokens = {'Tangshan': [('5', 'n5')], 'Earthquake': [('6', 'n5')], '7': [('9', 'd3')],
'28': [('10', 'd3')], 'p5': ['17', '17'], 'd2': ['2'], 'q': ['19'],
'p2': ['21'], 'p3': ['23'],
'p4': ['31'], 's': ['29'],
'h': ['27'], 'd': ['15'], 'a': ['25']}
expected_amr.node_to_tokens = {'Tangshan': [('5', 'n5')], 'Earthquake': [('6', 'n5')], '7': [('9', 'd3')],
'28': [('10', 'd3')], 'p5': ['17', '17'], 'd2': ['2'], 'q': ['19'],
's': ['29'], 'h': ['27'], 'd': ['15'], 'a': ['25'], 'p2': ['21'],
'p3': ['23'], 'p4': ['31']}
expected_amr.relation_to_tokens = {'month': [('9', 'd3')], 'time-of': [('3', 'd2')], 'poss': [('18', 'q')],
'location': [('16', 'd')], 'prep-on': [('0', 'p'), ('14', 'p')],
'domain': [('8', 'p'), ('18', 'p'), ('20', 'p')]}
expected_amr['Tangshan'] = {}
expected_amr['n5'] = {'op1': [('Tangshan',)], 'op2': [('Earthquake',)]}
expected_amr['Earthquake'] = {}
expected_amr['1976_Tangshan_earthquake'] = {}
expected_amr['e'] = {'wiki': [('1976_Tangshan_earthquake',)], 'name': [('n5',)]}
expected_amr['7'] = {}
expected_amr['d3'] = {'month': [('7',)], 'day': [('28',)]}
expected_amr['28'] = {}
expected_amr['m'] = {'ARG2': [('d3',)]}
expected_amr['d2'] = {'time-of': [('e',)], 'ARG1-of': [('m',)]}
expected_amr['p5'] = {}
expected_amr['q'] = {'poss': [('p5',)]}
expected_amr['d'] = {'time': [('d2',)], 'location': [('q',)]}
expected_amr['p2'] = {}
expected_amr['p3'] = {}
expected_amr['c'] = {}
expected_amr['s'] = {'mod': [('c',)]}
expected_amr['h'] = {'ARG1': [('p5',)], 'ARG2': [('s',)]}
expected_amr['p4'] = {'ARG0-of': [('h',)]}
expected_amr['a'] = {'op1': [('p2',)], 'op2': [('p3',)], 'op3': [('p4',)]}
expected_amr['p'] = {'prep-on': [('d',)], 'domain': [('a',)]}
assert generated_sentence == expected_sentence
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_metadata == expected_metadata
def test_train_pre_processing_ex_person():
amr = AMR()
amr.roots = ['s']
amr.reentrance_triples = [('h2', 'ARG1', 'p2'), ('f', 'ARG1', 'p2'), ('b', 'ARG1', 'p2')]
amr.node_to_concepts = {'n': 'name', 'c': 'comrade', 'h': 'have-rel-role-91', 'p': 'person', 'n2': 'name',
'p2': 'political-party', 'o2': 'overthrow-01', 'h2': 'have-org-role-91', 'p3': 'person',
'f': 'fall-05', 'b': 'bring-down-03', 'a': 'and', 'o': 'once', 's': 'say-01'}
amr.node_to_tokens = {'Deng': [('1', 'n')], 'Xiaoping': [('2', 'n')],
'c': ['0'], 'Communist': [('7', 'n2')], 'Party': [('8', 'n2')],
'-': [('10', 'o2')], 'p2': ['26', '15', '18'], 'f': ['16'],
'o2': ['12'], 'b': ['21', '22'], 'o': ['3'], 's': ['4']}
amr.relation_to_tokens = {'polarity': [('10', 'o2')], 'condition': [('14', 'b')], 'ARG1': [('5', 's')],
'name': [('1', 'p')]}
amr['Deng'] = {}
amr['n'] = {'op1': [('Deng',)], 'op2': [('Xiaoping',)]}
amr['Xiaoping'] = {}
amr['c'] = {}
amr['h'] = {'ARG2': [('c',)]}
amr['Deng_Xiaoping'] = {}
amr['p'] = {'wiki': [('Deng_Xiaoping',)], 'name': [('n',)], 'ARG0-of': [('h',)]}
amr['Communist'] = {}
amr['n2'] = {'op1': [('Communist',)], 'op2': [('Party',)]}
amr['Party'] = {}
amr['Communist_Party_of_China'] = {}
amr['p2'] = {'wiki': [('Communist_Party_of_China',)], 'name': [('n2',)]}
amr['-'] = {}
amr['o2'] = {'polarity': [('-',)], 'ARG1': [('p2',)]}
amr['h2'] = {'ARG1': [('p2',)]}
amr['p3'] = {'ARG0-of': [('h2',)]}
amr['f'] = {'ARG1': [('p2',)]}
amr['b'] = {'ARG0': [('p3',)], 'ARG1': [('p2',)], 'condition': [('f',)]}
amr['a'] = {'op1': [('o2',)], 'op2': [('b',)]}
amr['o'] = {}
amr['s'] = {'ARG0': [('p',)], 'ARG1': [('a',)], 'time': [('o',)]}
sentence = 'Comrade Deng Xiaoping once said that the Communist Party will not be overthrown - ' \
'if it falls , it will be brought down from within the party itself .'
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
expected_sentence = 'Comrade PERSON once said that the Communist Party will not be overthrown - ' \
'if it falls , it will be brought down from within the party itself .'
expected_metadata = {1: ['Deng', 'Xiaoping']}
expected_amr = AMR()
expected_amr.roots = ['s']
expected_amr.reentrance_triples = [('h2', 'ARG1', 'p2'), ('f', 'ARG1', 'p2'), ('b', 'ARG1', 'p2')]
expected_amr.node_to_concepts = {'c': 'comrade', 'h': 'have-rel-role-91', 'p': 'PERSON', 'n2': 'name',
'p2': 'political-party', 'o2': 'overthrow-01', 'h2': 'have-org-role-91',
'p3': 'person',
'f': 'fall-05', 'b': 'bring-down-03', 'a': 'and', 'o': 'once', 's': 'say-01'}
expected_amr.node_to_tokens = {'p': ['1'],
'c': ['0'], 'Communist': [('6', 'n2')], 'Party': [('7', 'n2')],
'-': [('9', 'o2')], 'p2': ['25', '14', '17'], 'f': ['15'],
'o2': ['11'], 'b': ['20', '21'], 'o': ['2'], 's': ['3']}
expected_amr.relation_to_tokens = {'polarity': [('9', 'o2')], 'condition': [('13', 'b')], 'ARG1': [('4', 's')]}
expected_amr['c'] = {}
expected_amr['h'] = {'ARG2': [('c',)]}
expected_amr['p'] = {'ARG0-of': [('h',)]}
expected_amr['Communist'] = {}
expected_amr['n2'] = {'op1': [('Communist',)], 'op2': [('Party',)]}
expected_amr['Party'] = {}
expected_amr['Communist_Party_of_China'] = {}
expected_amr['p2'] = {'wiki': [('Communist_Party_of_China',)], 'name': [('n2',)]}
expected_amr['-'] = {}
expected_amr['o2'] = {'polarity': [('-',)], 'ARG1': [('p2',)]}
expected_amr['h2'] = {'ARG1': [('p2',)]}
expected_amr['p3'] = {'ARG0-of': [('h2',)]}
expected_amr['f'] = {'ARG1': [('p2',)]}
expected_amr['b'] = {'ARG0': [('p3',)], 'ARG1': [('p2',)], 'condition': [('f',)]}
expected_amr['a'] = {'op1': [('o2',)], 'op2': [('b',)]}
expected_amr['o'] = {}
expected_amr['s'] = {'ARG0': [('p',)], 'ARG1': [('a',)], 'time': [('o',)]}
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_sentence == expected_sentence
assert generated_metadata == expected_metadata
def test_train_pre_processing_ex_3_person():
sentence = 'But we can see such philanthropists as Bill Gates , Li Ka @-@ shing , Jackie Chan have not affected ' \
'the growth of their enterprises in any way . Instead , they have actually increased the ' \
'competitiveness of their companies . '
amr: AMR = AMR()
amr.roots = ['m']
amr.reentrance_triples = [('e', 'poss', 'p2'), ('c3', 'poss', 't')]
amr.node_to_concepts = {'w': 'we', 'n': 'name', 'p3': 'person', 'n2': 'name', 'p4': 'person',
'n3': 'name', 'p5': 'person', 'a3': 'and', 'p2': 'philanthropist',
'e': 'enterprise', 'g': 'grow-01', 'a2': 'any', 'w2': 'way', 'a': 'affect-01',
's': 'see-01', 'p': 'possible-01', 'c': 'contrast-01', 't': 'they', 'c3': 'company',
'c2': 'competitiveness', 'a4': 'actual-02', 'i2': 'instead-of-91', 'i': 'increase-01',
'm': 'multi-sentence'}
amr.node_to_tokens = {'Bill': [('7', 'n')], 'Gates': [('8', 'n')], 'Li': [('10', 'n2')],
'Ka-shing': [('11', 'n2'), ('13', 'n2')],
'Jackie': [('15', 'n3')], 'Chan': [('16', 'n3')], 'e': ['24'], 'a2': ['26'],
'-': [('18', 'a')],
'p2': ['5'], 'g': ['21'], 'w2': ['27'], 'w': ['1'], 'a': ['19'], 's': ['3'],
'p': ['2'],
't': ['38', '23', '31'], 'c3': ['39'], 'c2': ['36'], 'a4': ['33'],
'i2': ['22', '29', '37'],
'c': ['0'], 'i': ['34']}
amr.relation_to_tokens = {'example': [('6', 'p2')], 'polarity': [('18', 'a')], 'manner': [('25', 'a')],
'poss': [('38', 'c3'), ('37', 'c2')]}
amr['w'] = {}
amr['Bill'] = {}
amr['n'] = {'op1': [('Bill',)], 'op2': [('Gates',)]}
amr['Gates'] = {}
amr['Bill_Gates'] = {}
amr['p3'] = {'wiki': [('Bill_Gates',)], 'name': [('n',)]}
amr['Li'] = {}
amr['n2'] = {'op1': [('Li',)], 'op2': [('Ka-shing',)]}
amr['Ka-shing'] = {}
amr['Li_Ka-shing'] = {}
amr['p4'] = {'wiki': [('Li_Ka-shing',)], 'name': [('n2',)]}
amr['Jackie'] = {}
amr['n3'] = {'op1': [('Jackie',)], 'op2': [('Chan',)]}
amr['Chan'] = {}
amr['Jackie_Chan'] = {}
amr['p5'] = {'wiki': [('Jackie_Chan',)], 'name': [('n3',)]}
amr['a3'] = {'op1': [('p3',)], 'op2': [('p4',)], 'op3': [('p5',)]}
amr['p2'] = {'example': [('a3',)]}
amr['e'] = {'poss': [('p2',)]}
amr['g'] = {'ARG1': [('e',)]}
amr['a2'] = {}
amr['w2'] = {'mod': [('a2',)]}
amr['-'] = {}
amr['a'] = {'polarity': [('-',)], 'ARG0': [('p2',)], 'ARG1': [('g',)], 'manner': [('w2',)]}
amr['s'] = {'ARG0': [('w',)], 'ARG1': [('a',)]}
amr['p'] = {'ARG1': [('s',)]}
amr['c'] = {'ARG2': [('p',)]}
amr['t'] = {}
amr['c3'] = {'poss': [('t',)]}
amr['c2'] = {'poss': [('c3',)]}
amr['a4'] = {'poss': [('c3',)]}
amr['i2'] = {}
amr['i'] = {'ARG0': [('t',)], 'ARG1': [('c2',)], 'ARG1-of': [('a4',), ('i2',)]}
amr['m'] = {'snt1': [('c',)], 'snt2': [('i',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# expected
expected_sentence = 'But we can see such philanthropists as PERSON , PERSON Ka @-@ shing , PERSON have not affected ' \
'the growth of their enterprises in any way . Instead , they have actually increased the ' \
'competitiveness of their companies .'
expected_metadata = {7: ['Bill', 'Gates'], 9: ['Li'], 14: ['Jackie', 'Chan']}
expected_amr: AMR = AMR()
expected_amr.roots = ['m']
expected_amr.reentrance_triples = [('e', 'poss', 'p2'), ('c3', 'poss', 't')]
expected_amr.node_to_concepts = {'w': 'we', 'p3': 'PERSON', 'p4': 'PERSON',
'p5': 'PERSON', 'a3': 'and', 'p2': 'philanthropist',
'e': 'enterprise', 'g': 'grow-01', 'a2': 'any', 'w2': 'way', 'a': 'affect-01',
's': 'see-01', 'p': 'possible-01', 'c': 'contrast-01', 't': 'they',
'c3': 'company',
'c2': 'competitiveness', 'a4': 'actual-02', 'i2': 'instead-of-91',
'i': 'increase-01',
'm': 'multi-sentence'}
expected_amr.node_to_tokens = {'p3': ['7'], 'p4': ['9'],
'p5': ['14'], 'e': ['22'], 'a2': ['24'],
'-': [('16', 'a')],
'p2': ['5'], 'g': ['19'], 'w2': ['25'], 'w': ['1'], 'a': ['17'], 's': ['3'],
'p': ['2'],
't': ['36', '21', '29'], 'c3': ['37'], 'c2': ['34'], 'a4': ['31'],
'i2': ['20', '27', '35'],
'c': ['0'], 'i': ['32']}
expected_amr.relation_to_tokens = {'example': [('6', 'p2')], 'polarity': [('16', 'a')], 'manner': [('23', 'a')],
'poss': [('36', 'c3'), ('35', 'c2')]}
expected_amr['w'] = {}
expected_amr['p3'] = {}
expected_amr['p4'] = {}
expected_amr['p5'] = {}
expected_amr['a3'] = {'op1': [('p3',)], 'op2': [('p4',)], 'op3': [('p5',)]}
expected_amr['p2'] = {'example': [('a3',)]}
expected_amr['e'] = {'poss': [('p2',)]}
expected_amr['g'] = {'ARG1': [('e',)]}
expected_amr['a2'] = {}
expected_amr['w2'] = {'mod': [('a2',)]}
expected_amr['-'] = {}
expected_amr['a'] = {'polarity': [('-',)], 'ARG0': [('p2',)], 'ARG1': [('g',)], 'manner': [('w2',)]}
expected_amr['s'] = {'ARG0': [('w',)], 'ARG1': [('a',)]}
expected_amr['p'] = {'ARG1': [('s',)]}
expected_amr['c'] = {'ARG2': [('p',)]}
expected_amr['t'] = {}
expected_amr['c3'] = {'poss': [('t',)]}
expected_amr['c2'] = {'poss': [('c3',)]}
expected_amr['a4'] = {'poss': [('c3',)]}
expected_amr['i2'] = {}
expected_amr['i'] = {'ARG0': [('t',)], 'ARG1': [('c2',)], 'ARG1-of': [('a4',), ('i2',)]}
expected_amr['m'] = {'snt1': [('c',)], 'snt2': [('i',)]}
assert generated_sentence == expected_sentence
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_metadata == expected_metadata
def test_train_pre_processing_ex_2_person():
sentence = 'the essay " Are you studying Paul or studying Bill ? ", she blathers that " people who study Paul ' \
'will become terrorists "; '
amr: AMR = AMR()
amr.roots = ['b2']
amr.reentrance_triples = [('o', 'op1', 'p3')]
amr.node_to_concepts = {'s2': 'she', 'n2': 'name', 'p3': 'person', 's3': 'study-01', 'p2': 'person',
't2': 'terrorist', 'b': 'become-01', 'y': 'you', 'n': 'name', 'p4': 'person',
'o': 'or', 's': 'study-01', 't': 'title-01', 'e': 'essay', 'b2': 'blather-01'}
amr.node_to_tokens = {'Paul': [('19', 'n2')], 's3': ['18'], 'p2': ['16'], 't2': ['22'],
'Bill': [('9', 'n')], 'p3': ['6'], 'interrogative': [('10', 's')], 'y': ['4'],
'o': ['7'], 's': ['5', '8'], 's2': ['12'], 'b': ['21'], 'e': ['1'], 'b2': ['13']}
amr.relation_to_tokens = {'mode': [('10', 's')]}
amr['s2'] = {}
amr['Paul'] = {}
amr['n2'] = {'op1': [('Paul',)]}
amr['-'] = {}
amr['p3'] = {'wiki': [('-',)], 'name': [('n2',)]}
amr['s3'] = {'ARG1': [('p3',)]}
amr['p2'] = {'ARG0-of': [('s3',)]}
amr['t2'] = {}
amr['b'] = {'ARG1': [('p2',)], 'ARG2': [('t2',)]}
amr['y'] = {}
amr['Bill'] = {}
amr['n'] = {'op1': [('Bill',)]}
amr['p4'] = {'wiki': [('-',)], 'name': [('n',)]}
amr['o'] = {'op1': [('p3',)], 'op2': [('p4',)]}
amr['interrogative'] = {}
amr['s'] = {'mode': [('interrogative',)], 'ARG0': [('y',)], 'ARG1': [('o',)]}
amr['t'] = {'ARG2': [('s',)]}
amr['e'] = {'ARG1-of': [('t',)]}
amr['b2'] = {'ARG0': [('s2',)], 'ARG1': [('b',)], 'medium': [('e',)]}
generated_amr, generated_sentence, generated_metadata = train_pre_processing(amr, sentence)
# Expected
expected_sentence = 'the essay " Are you studying PERSON or studying PERSON ? ", she blathers that " people who ' \
'study PERSON ' \
'will become terrorists ";'
expected_metadata = {6: ['Paul'], 9: ['Bill'], 19: ['Paul']}
expected_amr: AMR = AMR()
expected_amr.roots = ['b2']
expected_amr.reentrance_triples = [('o', 'op1', 'p3')]
expected_amr.node_to_concepts = {'s2': 'she', 'p3': 'PERSON', 's3': 'study-01', 'p2': 'person',
't2': 'terrorist', 'b': 'become-01', 'y': 'you', 'p4': 'PERSON',
'o': 'or', 's': 'study-01', 't': 'title-01', 'e': 'essay', 'b2': 'blather-01'}
expected_amr.node_to_tokens = {'s3': ['18'], 'p2': ['16'], 't2': ['22'],
'p4': ['9'], 'p3': ['6'], 'interrogative': [('10', 's')], 'y': ['4'],
'o': ['7'], 's': ['5', '8'], 's2': ['12'], 'b': ['21'], 'e': ['1'], 'b2': ['13']}
expected_amr.relation_to_tokens = {'mode': [('10', 's')]}
expected_amr['s2'] = {}
expected_amr['p3'] = {}
expected_amr['s3'] = {'ARG1': [('p3',)]}
expected_amr['p2'] = {'ARG0-of': [('s3',)]}
expected_amr['t2'] = {}
expected_amr['b'] = {'ARG1': [('p2',)], 'ARG2': [('t2',)]}
expected_amr['y'] = {}
expected_amr['p4'] = {}
expected_amr['o'] = {'op1': [('p3',)], 'op2': [('p4',)]}
expected_amr['interrogative'] = {}
expected_amr['s'] = {'mode': [('interrogative',)], 'ARG0': [('y',)], 'ARG1': [('o',)]}
expected_amr['t'] = {'ARG2': [('s',)]}
expected_amr['e'] = {'ARG1-of': [('t',)]}
expected_amr['b2'] = {'ARG0': [('s2',)], 'ARG1': [('b',)], 'medium': [('e',)]}
assert_amr_graph_dictionaries(expected_amr, generated_amr)
assert generated_sentence == expected_sentence
assert generated_metadata == expected_metadata