def extract_full_name(nlp_doc):
pattern = [{'POS': 'PROPN'}, {'POS': 'PROPN'}]
matcher.add('FULL_NAME', None, pattern)
matches = matcher(nlp_doc)
for match_id, start, end in matches:
span = nlp_doc[start:end]
return span.text
def emojis(self):
"""
Emojis detected using SpaCy matcher over the cleaned content, with unicode name and
sentiment score.
>>> Doc('Test with emoji 😀 😋 ').emojis
[('😀', 'GRINNING FACE', 0.571753986332574), ('😋', 'FACE SAVOURING DELICIOUS FOOD', 0.6335149863760218)]
"""
matcher = spacy.matcher.Matcher(self._spacy_doc.vocab)
for emoji, unicode_name in emoji2unicode_name.items():
matcher.add(unicode_name, None, ({'ORTH': emoji}, ))
return [(emoji, unicode_name, emoji2sentiment[emoji])
for emoji, unicode_name in self.match(matcher)]
def emojis(self):
"""
Emojis detected using SpaCy matcher over the cleaned content, with unicode name and
sentiment score.
>>> from pprint import pprint
>>> from textpipe.doc import Doc
>>> pprint(Doc('Test with emoji 😀 😋 ').emojis)
[('😀', 'GRINNING FACE', 0.571753986332574),
('😋', 'FACE SAVOURING DELICIOUS FOOD', 0.6335149863760218)]
"""
matcher = spacy.matcher.Matcher(self._spacy_doc.vocab)
for emoji, unicode_name in EMOJI_TO_UNICODE_NAME.items():
matcher.add(unicode_name, None, ({'ORTH': emoji}, ))
return [(emoji, unicode_name, EMOJI_TO_SENTIMENT[emoji])
for emoji, unicode_name in self.match(matcher)]
def extract_phone_number(nlp_doc):
pattern = [{
'ORTH': '('
}, {
'SHAPE': 'ddd'
}, {
'ORTH': ')'
}, {
'SHAPE': 'ddd'
}, {
'ORTH': '-',
'OP': '?'
}, {
'SHAPE': 'ddd'
}]
matcher.add('PHONE_NUMBER', None, pattern)
matches = matcher(nlp_doc)
for match_id, start, end in matches:
span = nlp_doc[start:end]
return span.text
def test_matcher_segfault():
nlp = spacy.load('en', parser=False, entity=False)
matcher = spacy.matcher.Matcher(nlp.vocab)
content = u'''a b; c'''
matcher.add(entity_key='1', label='TEST', attrs={}, specs=[[{ORTH: 'a'}, {ORTH: 'b'}]])
matcher(nlp(content))
matcher.add(entity_key='2', label='TEST', attrs={}, specs=[[{ORTH: 'a'}, {ORTH: 'b'}, {IS_PUNCT: True}, {ORTH: 'c'}]])
matcher(nlp(content))
matcher.add(entity_key='3', label='TEST', attrs={}, specs=[[{ORTH: 'a'}, {ORTH: 'b'}, {IS_PUNCT: True}, {ORTH: 'd'}]])
matcher(nlp(content))
def test_matcher_segfault():
nlp = spacy.load('en', parser=False, entity=False)
matcher = spacy.matcher.Matcher(nlp.vocab)
content = u'''a b; c'''
matcher.add(entity_key='1',
label='TEST',
attrs={},
specs=[[{
ORTH: 'a'
}, {
ORTH: 'b'
}]])
matcher(nlp(content))
matcher.add(entity_key='2',
label='TEST',
attrs={},
specs=[[{
ORTH: 'a'
}, {
ORTH: 'b'
}, {
IS_PUNCT: True
}, {
ORTH: 'c'
}]])
matcher(nlp(content))
matcher.add(entity_key='3',
label='TEST',
attrs={},
specs=[[{
ORTH: 'a'
}, {
ORTH: 'b'
}, {
IS_PUNCT: True
}, {
ORTH: 'd'
}]])
matcher(nlp(content))
def main():
in_file = codecs.open('Mercier_1600-1837.txt').read()
relationship_set = RelationshipHandler()
merger = quotemerger.HyphenatedNameMerger(nlp.vocab)
nlp.add_pipe(merger.merger, first=True)
matcher.add('FATHER_SON_1', relationship_set.handle_fs_1,
MATCHERS['FATHER_SON_1'])
matcher.add('FATHER_SON_2', relationship_set.handle_fs_2,
MATCHERS['FATHER_SON_2'])
matcher.add('FATHER_SON_3', relationship_set.handle_fs_3,
MATCHERS['FATHER_SON_3'])
matcher.add('FATHER_DAUGHTER_1', relationship_set.handle_fd_1,
MATCHERS['FATHER_DAUGHTER_1'])
matcher.add('FATHER_DAUGHTER_2', relationship_set.handle_fd_2,
MATCHERS['FATHER_DAUGHTER_2'])
matcher.add('FATHER_DAUGHTER_3', relationship_set.handle_fd_3,
MATCHERS['FATHER_DAUGHTER_3'])
matcher.add('FATHER_DAUGHTER_4', relationship_set.handle_fd_4,
MATCHERS['FATHER_DAUGHTER_4'])
matcher.add('MARIAGE_1', relationship_set.handle_mariage_1_and_2,
MATCHERS['MARIAGE_1'])
matcher.add('MARIAGE_2', relationship_set.handle_mariage_1_and_2,
MATCHERS['MARIAGE_2'])
matcher.add('MARIAGE_3', relationship_set.handle_mariage_3_and_4,
MATCHERS['MARIAGE_3'])
matcher.add('MARIAGE_4', relationship_set.handle_mariage_3_and_4,
MATCHERS['MARIAGE_4'])
matcher.add('MARIAGE_5', relationship_set.handle_mariage_5,
MATCHERS['MARIAGE_5'])
matcher.add('MARIAGE_6', relationship_set.handle_mariage_6,
MATCHERS['MARIAGE_6'])
matcher.add('GENDRE_1', relationship_set.handle_gendre_1,
MATCHERS['GENDRE_1'])
matcher.add('GENDRE_2', relationship_set.handle_gendre_2,
MATCHERS['GENDRE_2'])
matcher.add('GENDRE_3', relationship_set.handle_gendre_3,
MATCHERS['GENDRE_3'])
matcher.add('PERE', relationship_set.handle_pere_1, MATCHERS['PERE'])
parsed_doc = nlp(in_file)
matches = matcher(parsed_doc)
out_file = codecs.open('relations.csv', 'w', encoding='utf8')
out_file.write('parent,child,relation type\n')
for rel in relationship_set.relationships:
if isinstance(rel, FatherSonRelationship):
out_file.write('{},{},son\n'.format(rel.father, rel.son))
print('{} is the father of {}'.format(rel.father, rel.son))
elif isinstance(rel, FatherDaughterRelationship):
out_file.write('{},{},daughter\n'.format(rel.father, rel.daughter))
print('{} is the father of {}'.format(rel.father, rel.daughter))
elif isinstance(rel, GendreRelationship):
out_file.write('{},{},daughter'.format(rel.father, rel.name))
out_file.write('{},{},spouse'.format(rel.husband, rel.name))
print('An unnamed woman is the daughter of {} and husband of {}'.
format(rel.father, rel.husband))
out_file.close()
print('Matchers done')