def append_ner(input_file=None, output_file=None, batch_size=None):
"""
Parameters
----------
Returns
-------
"""
input = open(input_file,
encoding='utf-8') if input_file is not None else sys.stdin
csv_reader = csv.reader(input)
column_mapper = CsvColumnMapper(next(csv_reader), ['ner'],
source_required=['sentence', 'words'])
mnofc = ManageNewOutputFileCreation(output_file, batch_size)
spacy_pipeline = en_core_web_sm.load()
for count, entry in enumerate(csv_reader, start=0):
new_file = mnofc.get_new_file_if_necessary()
if new_file:
csv_writer = csv.writer(new_file)
csv_writer.writerow(column_mapper.get_new_headers())
# now that we've finished creating a new file as necessary, we can proceed with the business
# at hand:
words = column_mapper.get_field_value_from_source(entry, 'words', True)
if words is None:
csv_writer.writerow(column_mapper.get_new_row_values(
entry, [None]))
continue
sentence = column_mapper.get_field_value_from_source(entry, 'sentence')
spacy_doc = spacy_pipeline(sentence)
spacy_tokens = [token.text for token in spacy_doc]
ner_lookup_spacy_tokenization = {}
for index, token in enumerate(spacy_doc, start=1):
if token.ent_type != 0: # in ['PERSON', 'ORG']:
ner_lookup_spacy_tokenization[index] = token.ent_type_
ner_lookup = ner_lookup_spacy_tokenization
tokens = [token for _, token in words]
if tokens != spacy_tokens and len(ner_lookup_spacy_tokenization) > 0:
ner_lookup = SyncTags.b_lookup_to_a_lookup(
tokens, spacy_tokens, ner_lookup_spacy_tokenization)
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [ner_lookup]))
def identify_ucca_paths(input, output):
csv_reader = csv.reader(input)
csv_writer = csv.writer(output)
column_mapper = CsvColumnMapper(
next(csv_reader),
target_columns=['path_id', 'path', 'comment'],
source_required=[
'id', 'sentence', 'ent1_start', 'ent1_end', 'ent2_start',
'ent2_end', 'ucca_parse', 'trigger_idx'
])
csv_writer.writerow(column_mapper.get_new_headers())
for counter, entry in enumerate(csv_reader, start=1):
print('Processing sentence #',
column_mapper.get_field_value_from_source(entry, 'id'))
ucca_parse_serialization = column_mapper.get_field_value_from_source(
entry, 'ucca_parse')
if ucca_parse_serialization is None:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, 'ucca_parse missing']))
continue
ucca_parse = UccaParsedPassage.from_serialization(
ucca_parse_serialization)
links = ucca_parse.get_links()
trigger_token_id = column_mapper.get_field_value_from_source(
entry, 'trigger_idx', as_int=True)
ent1_start_token_id = column_mapper.get_field_value_from_source(
entry, 'ent1_start', as_int=True)
ent2_start_token_id = column_mapper.get_field_value_from_source(
entry, 'ent2_start', as_int=True)
if trigger_token_id is None or ent1_start_token_id is None or ent2_start_token_id is None:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, 'indices missing']))
continue
trigger_node_id = ucca_parse.get_node_id_by_token_id(trigger_token_id)
trigger_parent_node_id = Link.get_parents(links, trigger_node_id)[0]
ent1_start_node_id = ucca_parse.get_node_id_by_token_id(
ent1_start_token_id)
ent1_parent_node_ids = Link.get_parents(links, ent1_start_node_id)
if len(ent1_parent_node_ids) == 0:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, 'Could not find parent of ent1']))
continue
ent1_parent_node_id = ent1_parent_node_ids[0]
ent2_start_node_id = ucca_parse.get_node_id_by_token_id(
ent2_start_token_id)
ent2_parent_node_ids = Link.get_parents(links, ent2_start_node_id)
if len(ent2_parent_node_ids) == 0:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, 'Could not find parent of ent2']))
continue
ent2_parent_node_id = ent2_parent_node_ids[0]
graph = DepGraph(links)
ent1_to_trigger_steps = graph.get_steps(ent1_parent_node_id,
trigger_parent_node_id)
ent1_to_trigger_strings = ucca_parse.get_path_representations(
ent1_to_trigger_steps)
trigger_to_ent2_steps = graph.get_steps(trigger_parent_node_id,
ent2_parent_node_id)
trigger_to_ent2_strings = ucca_parse.get_path_representations(
trigger_to_ent2_steps)
sentence_id = column_mapper.get_field_value_from_source(entry,
'id',
as_int=True)
for count, (segment1,
segment2) in enumerate(product(ent1_to_trigger_strings,
trigger_to_ent2_strings),
start=1):
path_id = '{0}_{1}'.format(sentence_id, count)
path = '{0} >< {1}'.format(segment1, segment2)
comment = None
csv_writer.writerow(
column_mapper.get_new_row_values(entry,
[path_id, path, comment]))
def parse_pss(port,
model_path,
input_file=None,
output_file=None,
batch_size=None):
"""
Parameters
----------
Returns
-------
"""
input = open(input_file,
encoding='utf-8') if input_file is not None else sys.stdin
csv_reader = csv.reader(input)
column_mapper = CsvColumnMapper(
next(csv_reader), ['pss'],
source_required=['sentence', 'ud_parse', 'words'])
batch = 0
output = None
output_file = output_file[:-len('.csv')] if output_file is not None and output_file.endswith('.csv') \
else output_file
print('BEGIN-INIT-PSS')
from models.supersenses.lstm_mlp_supersenses_model import LstmMlpSupersensesModel
from models.supersenses.preprocessing import preprocess_sentence
from models.supersenses.preprocessing.corenlp import CoreNLPServer
corenlp = CoreNLPServer()
corenlp.start(port)
model = LstmMlpSupersensesModel.load(model_path)
print('END-INIT-PSS')
for count, entry in enumerate(csv_reader, start=0):
# the next few lines of code deal with opening and closing files (depending on the batching argument, etc)
new_file = False
# first option: standard output ...
if count == 0 and output_file is None:
output = sys.stdout
new_file = True
# second option: we've just started, we're writing to a real file, but no batching
if count == 0 and output_file is not None and batch_size is None:
output_file_actual = '{0}.csv'.format(output_file)
output = open(output_file_actual,
'w',
encoding='utf-8',
newline='')
new_file = True
# second case: we've finished a batch (and we are batching..)
if batch_size is not None and count % batch_size == 0:
output_file_actual = '{0}-{1}.csv'.format(output_file, batch)
if output is not None:
output.close()
output = open(output_file_actual,
'w',
encoding='utf-8',
newline='')
batch += 1
new_file = True
# if we did create a new file, let's ensure that the first row consists of column titles
if new_file:
csv_writer = csv.writer(output)
csv_writer.writerow(column_mapper.get_new_headers())
# now that we've finished creating a new file as necessary, we can proceed with the business
# at hand:
words = column_mapper.get_field_value_from_source(entry, 'words', True)
if words is None:
csv_writer.writerow(column_mapper.get_new_row_values(
entry, [None]))
continue
sentence = column_mapper.get_field_value_from_source(entry, 'sentence')
proper_tokens = word_tokenize(sentence)
print('BEGIN-PROCESS-PSS')
preprocessed = preprocess_sentence(' '.join(proper_tokens))
pss_pred = model.predict(
preprocessed.xs, [x.identified_for_pss for x in preprocessed.xs])
print('END-PROCESS-PSS')
pss_lookup_nltk_tokens = {}
for index in range(len(preprocessed.xs)):
if pss_pred[index].supersense_role:
pss_lookup_nltk_tokens[index +
1] = (pss_pred[index].supersense_role,
pss_pred[index].supersense_func)
ud_tokens = [token for _, token in words]
pss_lookup = pss_lookup_nltk_tokens
if ud_tokens != proper_tokens and len(pss_lookup_nltk_tokens) > 0:
pss_lookup = SyncTags.b_lookup_to_a_lookup(ud_tokens,
proper_tokens,
pss_lookup_nltk_tokens)
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [pss_lookup]))
corenlp.stop()
def parse_ucca(model_prefix, input_file=None, output_file=None, batch_size=None):
"""
Parameters
----------
Returns
-------
"""
input = open(input_file, encoding='utf-8') if input_file is not None else sys.stdin
csv_reader = csv.reader(input)
column_mapper = CsvColumnMapper(
source_first_row=next(csv_reader),
target_columns=
['id',
'sentence',
'ent1',
'ent2',
'ent1_start',
'ent1_end',
'ent2_start',
'ent2_end',
'ucca_parse',
'words',
'lemmas',
'comment'],
source_required=
['tac_tokens',
'subj_start',
'subj_end',
'obj_start',
'obj_end'],
filter_source_from_result=
['subj_start',
'subj_end',
'obj_start',
'obj_end']
)
detokenizer = Detokenizer()
mnofc = ManageNewOutputFileCreation(output_file, batch_size)
print('BEGIN-INIT-TUPA')
parser = TupaParser(model_prefix)
print('END-INIT-TUPA')
for count, entry in enumerate(csv_reader, start=0):
new_file = mnofc.get_new_file_if_necessary()
if new_file:
csv_writer = csv.writer(new_file)
csv_writer.writerow(column_mapper.get_new_headers())
tac_tokens = eval(column_mapper.get_field_value_from_source(entry, 'tac_tokens'))
sentence = detokenizer.detokenize(tac_tokens)
print('BEGIN-PROCESS-TUPA')
parsed_sentence = parser.parse_sentence(sentence)
print('END-PROCESS-TUPA')
tokens = []
tokens_with_indices = []
lemmas_with_indices = []
for ucca_terminal in parsed_sentence.terminals:
tokens.append(ucca_terminal.text)
tokens_with_indices.append((ucca_terminal.token_id, ucca_terminal.text))
lemmas_with_indices.append((ucca_terminal.token_id, ucca_terminal.lemma))
tac_tokens_lookup = {}
tac_tokens_lookup['subj_start'] = int(column_mapper.get_field_value_from_source(entry, 'subj_start'))
tac_tokens_lookup['subj_end'] = int(column_mapper.get_field_value_from_source(entry, 'subj_end'))
tac_tokens_lookup['obj_start'] = int(column_mapper.get_field_value_from_source(entry, 'obj_start'))
tac_tokens_lookup['obj_end'] = int(column_mapper.get_field_value_from_source(entry, 'obj_end'))
token_lookup = SyncTacTags.b_lookup_to_a_lookup(tokens, tac_tokens, tac_tokens_lookup)
if len(token_lookup) != len(tac_tokens_lookup):
csv_writer.writerow(column_mapper.get_new_row_values(entry,
[count,
sentence,
None,
None,
None,
None,
None,
None,
None,
None,
None,
'was not able to reconcile TAC and Tupa\'s Spacy based indexing']))
continue
ent1_start = token_lookup['subj_start']
ent1_end = token_lookup['subj_end']
ent1 = ' '.join(tokens[ent1_start:ent1_end + 1])
ent2_start = token_lookup['obj_start']
ent2_end = token_lookup['obj_end']
ent2 = ' '.join(tokens[ent2_start:ent2_end + 1])
csv_writer.writerow(column_mapper.get_new_row_values(entry,
[count,
sentence,
ent1,
ent2,
ent1_start + 1,
ent1_end + 1,
ent2_start + 1,
ent2_end + 1,
parsed_sentence.serialize(),
tokens_with_indices,
lemmas_with_indices,
None]))
def filter_relations(input, output, entity_types=None):
csv_reader = csv.reader(input)
csv_writer = csv.writer(output)
required_columns = [
'id', 'sentence', 'words', 'lemmas', 'ent1_start', 'ent1_end',
'ent2_start', 'ent2_end', 'path'
]
if entity_types is not None:
required_columns.append('ner')
column_mapper = CsvColumnMapper(source_first_row=next(csv_reader),
target_columns=[],
source_required=required_columns)
csv_writer.writerow(column_mapper.get_new_headers())
for counter, entry in enumerate(csv_reader, start=1):
path = column_mapper.get_field_value_from_source(entry, 'path')
if path is None or path == '':
continue
ent1_start = column_mapper.get_field_value_from_source(entry,
'ent1_start',
as_int=True)
ent1_end = column_mapper.get_field_value_from_source(entry,
'ent1_end',
as_int=True)
ent1_indexes = [idx for idx in range(ent1_start, ent1_end + 1)]
ent2_start = column_mapper.get_field_value_from_source(entry,
'ent2_start',
as_int=True)
ent2_end = column_mapper.get_field_value_from_source(entry,
'ent2_end',
as_int=True)
ent2_indexes = [idx for idx in range(ent2_start, ent2_end + 1)]
filtered = False
if entity_types is not None:
entity1_type = entity_types[0]
entity2_type = entity_types[1]
ner_tags = column_mapper.get_field_value_from_source(entry,
'ner',
evaluate=True)
# let's see if any of entity 1's tokens match entity1_type
entity1_type_match = False
for ent1_index in ent1_indexes:
if ent1_index in ner_tags and ner_tags[
ent1_index] == entity1_type:
entity1_type_match = True
break
entity2_type_match = False
for ent2_index in ent2_indexes:
if ent2_index in ner_tags and ner_tags[
ent2_index] == entity2_type:
entity2_type_match = True
break
filtered = not entity1_type_match or not entity2_type_match
if not filtered:
csv_writer.writerow(column_mapper.get_new_row_values(entry, []))
output.close()
def parse_ud(input_file=None, output_file=None, batch_size=None):
"""
Parameters
----------
Returns
-------
"""
input = open(input_file,
encoding='utf-8') if input_file is not None else sys.stdin
csv_reader = csv.reader(input)
column_mapper = CsvColumnMapper(source_first_row=next(csv_reader),
target_columns=[
'id', 'sentence', 'ent1', 'ent2',
'ent1_start', 'ent1_end', 'ent2_start',
'ent2_end', 'ud_parse', 'words',
'lemmas', 'comment'
],
source_required=[
'tac_tokens', 'subj_start', 'subj_end',
'obj_start', 'obj_end'
],
filter_source_from_result=[
'subj_start', 'subj_end', 'obj_start',
'obj_end'
])
detokenizer = Detokenizer()
## The prints before and after Pipeline initialization are used by the calling script
## as markets to indicated output that should be filtered out - see bin/parse_ud
## implementation
print('BEGIN-INIT-NLP')
nlp = stanfordnlp.Pipeline()
print('END-INIT-NLP')
batch = 0
output = None
output_file = output_file[:-len('.csv')] if output_file is not None and output_file.endswith('.csv') \
else output_file
for count, entry in enumerate(csv_reader, start=0):
# the next few lines of code deal with opening and closing files (depending on the batching argument, etc)
new_file = False
# first option: standard output ...
if count == 0 and output_file is None:
output = sys.stdout
new_file = True
# second option: we've just started, we're writing to a real file, but no batching
if count == 0 and output_file is not None and batch_size is None:
output_file_actual = '{0}.csv'.format(output_file)
output = open(output_file_actual,
'w',
encoding='utf-8',
newline='')
new_file = True
# third option: we've finished a batch (and we are batching..)
if output_file is not None and batch_size is not None and count % batch_size == 0:
output_file_actual = '{0}-{1}.csv'.format(output_file, batch)
if output is not None:
output.close()
output = open(output_file_actual,
'w',
encoding='utf-8',
newline='')
batch += 1
new_file = True
# if we did create a new file, let's ensure that the first row consists of column titles
if new_file:
csv_writer = csv.writer(output)
csv_writer.writerow(column_mapper.get_new_headers())
# now that we've finished creating a new file as necessary, we can proceed with the business
# at hand:
tac_tokens = eval(
column_mapper.get_field_value_from_source(entry, 'tac_tokens'))
sentence = detokenizer.detokenize(tac_tokens)
parsed_sentence = nlp(sentence)
# let's ignore sentences who parse into multiple sentences - so as to avoid confusion
if len(parsed_sentence.sentences) > 1:
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, None, None, None, None, None, None, None,
None, None,
'python stanfordnlp parse produced more than one sentence'
]))
continue
ud_parse = []
for governor, dep, word in parsed_sentence.sentences[0].dependencies:
ud_parse.append(
(word.index, word.text, dep, governor.index, governor.text))
tokens = []
tokens_with_indices = []
lemmas_with_indices = []
for token in parsed_sentence.sentences[0].tokens:
for word in token.words:
tokens.append(word.text)
tokens_with_indices.append((word.index, word.text))
lemmas_with_indices.append((word.index, word.lemma))
ud_parse.sort(key=lambda x: int(x[0]))
tac_tokens_lookup = {}
tac_tokens_lookup['subj_start'] = int(
column_mapper.get_field_value_from_source(entry, 'subj_start'))
tac_tokens_lookup['subj_end'] = int(
column_mapper.get_field_value_from_source(entry, 'subj_end'))
tac_tokens_lookup['obj_start'] = int(
column_mapper.get_field_value_from_source(entry, 'obj_start'))
tac_tokens_lookup['obj_end'] = int(
column_mapper.get_field_value_from_source(entry, 'obj_end'))
token_lookup = SyncTacTags.b_lookup_to_a_lookup(
tokens, tac_tokens, tac_tokens_lookup)
if len(token_lookup) != len(tac_tokens_lookup):
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, None, None, None, None, None, None, None,
None, None,
'was not able to reconcile TAC and python stanfordnlp parse indexing'
]))
continue
ent1_start = token_lookup['subj_start']
ent1_end = token_lookup['subj_end']
ent1 = ' '.join(tokens[ent1_start:ent1_end + 1])
ent2_start = token_lookup['obj_start']
ent2_end = token_lookup['obj_end']
ent2 = ' '.join(tokens[ent2_start:ent2_end + 1])
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, ent1, ent2, ent1_start + 1, ent1_end + 1,
ent2_start + 1, ent2_end + 1, ud_parse, tokens_with_indices,
lemmas_with_indices, None
]))
def extract_relations_ud(input, output, triggers, paths, include_miss=False):
csv_reader = csv.reader(input)
csv_writer = csv.writer(output)
required_columns = [
'sentence', 'ud_parse', 'lemmas', 'ent1_start', 'ent1_end',
'ent2_start', 'ent2_end'
]
column_mapper = CsvColumnMapper(
next(csv_reader),
['trigger', 'trigger_idx', 'path', 'extraction_comment'],
source_required=required_columns)
csv_writer.writerow(column_mapper.get_new_headers())
for counter, entry in enumerate(csv_reader, start=1):
ud_parse = column_mapper.get_field_value_from_source(entry,
'ud_parse',
evaluate=True)
if ud_parse is None:
if include_miss:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, None, 'ud_parse missing']))
continue
links = UdRepresentationPlaceholder.get_links_from_ud_dep(ud_parse)
lemma_indices = column_mapper.get_field_value_from_source(
entry, 'lemmas', evaluate=True)
lemmas = [lemma for _, lemma in lemma_indices]
word_indices = column_mapper.get_field_value_from_source(entry,
'words',
evaluate=True)
words = [word for _, word in word_indices]
ent1_start = column_mapper.get_field_value_from_source(entry,
'ent1_start',
as_int=True)
ent1_end = column_mapper.get_field_value_from_source(entry,
'ent1_end',
as_int=True)
if ent1_start is None or ent1_end is None:
if include_miss:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, None, 'could not identify ent1']))
continue
ent1_indexes = [idx for idx in range(ent1_start, ent1_end + 1)]
ent1_head = Link.get_head(links, ent1_indexes)
ent2_start = column_mapper.get_field_value_from_source(entry,
'ent2_start',
as_int=True)
ent2_end = column_mapper.get_field_value_from_source(entry,
'ent2_end',
as_int=True)
if ent2_start is None or ent2_end is None:
if include_miss:
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, None, 'could not identify ent2']))
continue
ent2_indexes = [idx for idx in range(ent2_start, ent2_end + 1)]
ent2_head = Link.get_head(links, ent2_indexes)
graph = DepGraph(links)
found_relation = False
trigger_word_matches = []
for trigger_index, (word, lemma) in enumerate(zip(words, lemmas),
start=1):
if word in triggers or lemma in triggers:
trigger_word_matches.append(word)
trigger_to_ent2 = Step.get_default_representation(
graph.get_steps(trigger_index, ent2_head))
ent1_to_trigger = Step.get_default_representation(
graph.get_steps(ent1_head, trigger_index))
ent1_to_ent2_via_trigger = '{0} >< {1}'.format(
ent1_to_trigger, trigger_to_ent2)
if ent1_to_ent2_via_trigger in paths:
found_relation = True
trigger = word if word in triggers else lemma
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [
trigger, trigger_index,
ent1_to_ent2_via_trigger, None
]))
break
if not found_relation:
if include_miss:
comment = 'relation not found - considered the following matching triggers: {}' \
.format(' '.join(trigger_word_matches))
csv_writer.writerow(
column_mapper.get_new_row_values(
entry, [None, None, None, comment]))
output.close()
def parse_ucca(tupa_dir,
model_prefix,
tupa_batch_size,
input_file=None,
output_file=None,
batch_size=None):
"""
Parameters
----------
Returns
-------
"""
input = open(input_file,
encoding='utf-8') if input_file is not None else sys.stdin
csv_reader = csv.reader(input)
column_mapper = CsvColumnMapper(source_first_row=next(csv_reader),
target_columns=[
'id', 'sentence', 'ent1', 'ent2',
'ent1_start', 'ent1_end', 'ent2_start',
'ent2_end', 'ucca_parse', 'words',
'lemmas', 'comment'
],
source_required=[
'tac_tokens', 'subj_start', 'subj_end',
'obj_start', 'obj_end'
],
filter_source_from_result=[
'subj_start', 'subj_end', 'obj_start',
'obj_end'
])
detokenizer = Detokenizer()
mnofc = ManageNewOutputFileCreation(output_file, batch_size)
parser = TupaParser2(tupa_dir, model_prefix)
nlp = spacy.load('en_core_web_md')
count = -1
for next_batch in zip_longest(*([csv_reader] * tupa_batch_size)):
entries = []
sentences = []
for entry in next_batch:
if entry is None:
#we've reached the end of the batch
break
entries.append(entry)
tac_tokens = eval(
column_mapper.get_field_value_from_source(entry, 'tac_tokens'))
sentence = detokenizer.detokenize(tac_tokens)
sentences.append(sentence)
# send multiple sentnces for parsing (as many as 'TUPA_BATCH_SIZE')
parsed_sentences = parser.parse_sentences(sentences)
# if the length of parsed_sentences is different to the length of sentences, then
# all bets are off - no point in trying to consolidate.
# (that's especially true as the situation in which parsed_sentences' lenght will be
# different is when the 'python -m tupa' command fails, in which case parsed_sentences will
# be empty
if len(parsed_sentences) != len(sentences):
parsed_sentences = [None] * len(sentences)
for sentence, parsed_sentence, entry in zip(sentences,
parsed_sentences, entries):
count += 1
new_file = mnofc.get_new_file_if_necessary()
if new_file:
csv_writer = csv.writer(new_file)
csv_writer.writerow(column_mapper.get_new_headers())
if parsed_sentence is None:
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, None, None, None, None, None, None,
None, None, None, 'TUPA did not produce a UCCA parse'
]))
continue
tokens = []
tokens_with_indices = []
lemmas_with_indices = []
for ucca_terminal in parsed_sentence.terminals:
tokens.append(ucca_terminal.text)
tokens_with_indices.append(
(ucca_terminal.token_id, ucca_terminal.text))
# use spacy to get lemmas
spacied = nlp(sentence)
for token_id, word in enumerate(spacied, start=1):
lemmas_with_indices.append((token_id, word.lemma_))
tac_tokens = eval(
column_mapper.get_field_value_from_source(entry, 'tac_tokens'))
tac_tokens_lookup = {}
tac_tokens_lookup['subj_start'] = int(
column_mapper.get_field_value_from_source(entry, 'subj_start'))
tac_tokens_lookup['subj_end'] = int(
column_mapper.get_field_value_from_source(entry, 'subj_end'))
tac_tokens_lookup['obj_start'] = int(
column_mapper.get_field_value_from_source(entry, 'obj_start'))
tac_tokens_lookup['obj_end'] = int(
column_mapper.get_field_value_from_source(entry, 'obj_end'))
token_lookup = SyncTacTags.b_lookup_to_a_lookup(
tokens, tac_tokens, tac_tokens_lookup)
if len(token_lookup) != len(tac_tokens_lookup):
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, None, None, None, None, None, None,
None, None, None,
'was not able to reconcile TAC and Tupa\'s Spacy based indexing'
]))
continue
ent1_start = token_lookup['subj_start']
ent1_end = token_lookup['subj_end']
ent1 = ' '.join(tokens[ent1_start:ent1_end + 1])
ent2_start = token_lookup['obj_start']
ent2_end = token_lookup['obj_end']
ent2 = ' '.join(tokens[ent2_start:ent2_end + 1])
csv_writer.writerow(
column_mapper.get_new_row_values(entry, [
count, sentence, ent1, ent2, ent1_start + 1, ent1_end + 1,
ent2_start + 1, ent2_end + 1,
parsed_sentence.serialize(), tokens_with_indices,
lemmas_with_indices, None
]))