def test_more_than_two_overlapping_predicates(self):
"""
Test whether the predictor can correctly consolidate multiword
predicates.
"""
tokenizer = WordTokenizer(word_splitter=SpacyWordSplitter(
pos_tags=True))
sent_tokens = tokenizer.tokenize(
"John refused to consider joining the club.")
# Emulate predications - for "refused" and "consider" and "joining"
predictions = [
[
"B-ARG0", "B-V", "B-ARG1", "I-ARG1", "I-ARG1", "I-ARG1",
"I-ARG1", "O"
],
[
"B-ARG0", "B-BV", "I-BV", "B-V", "B-ARG1", "I-ARG1", "I-ARG1",
"O"
],
["B-ARG0", "B-BV", "I-BV", "I-BV", "B-V", "B-ARG1", "I-ARG1", "O"],
]
# Consolidate
pred_dict = consolidate_predictions(predictions, sent_tokens)
# Check that only "refused to consider to join" is left
assert len(pred_dict) == 1
tags = list(pred_dict.values())[0]
assert get_predicate_text(sent_tokens,
tags) == "refused to consider joining"
def test_predicate_consolidation(self):
"""
Test whether the predictor can correctly consolidate multiword
predicates.
"""
tokenizer = WordTokenizer(word_splitter=SpacyWordSplitter(
pos_tags=True))
sent_tokens = tokenizer.tokenize(
"In December, John decided to join the party.")
# Emulate predications - for both "decided" and "join"
predictions = [
[
"B-ARG2", "I-ARG2", "O", "B-ARG0", "B-V", "B-ARG1", "I-ARG1",
"I-ARG1", "I-ARG1", "O"
],
[
"O", "O", "O", "B-ARG0", "B-BV", "I-BV", "B-V", "B-ARG1",
"I-ARG1", "O"
],
]
# Consolidate
pred_dict = consolidate_predictions(predictions, sent_tokens)
# Check that only "decided to join" is left
assert len(pred_dict) == 1
tags = list(pred_dict.values())[0]
assert get_predicate_text(sent_tokens, tags) == "decided to join"
def format_extractions(sent_tokens, sent_predictions):
"""
Convert token-level raw predictions to clean extractions.
"""
# Consolidate predictions
if not (len(set(map(len, sent_predictions))) == 1):
raise AssertionError
assert len(sent_tokens) == len(sent_predictions[0])
sent_str = " ".join(map(str, sent_tokens))
pred_dict = consolidate_predictions(sent_predictions, sent_tokens)
# Build and return output dictionary
results = []
all_tags = []
results_dict = {'verbs':[], 'words': [str(token) for token in sent_tokens]}
for tags in pred_dict.values():
# Join multi-word predicates
tags = join_mwp(tags)
all_tags.append(tags)
# Create description text
oie_frame = get_oie_frame(sent_tokens, tags)
# Add a predicate prediction to outputs.
results.append("\t".join([sent_str, get_frame_str(oie_frame)]))
results_dict['verbs'].append({'tags': tags})
return results, all_tags, results_dict
def consolidate(ie_res):
# consolidate ie results
ie_slim = []
for ie in ie_res:
outputs = [v['tags'] for v in ie['verbs']]
sent_tokens = [Token(w) for w in ie['words']]
consolidated = oie.consolidate_predictions(outputs, sent_tokens)
tags = [t for v, t in consolidated.items()]
# tags = [t for v,t in ie.items()]
ie_slim.append({'words': sent_tokens, 'tags': tags})
return ie_slim
def test_more_than_two_overlapping_predicates(self):
"""
Test whether the predictor can correctly consolidate multiword
predicates.
"""
tokenizer = WordTokenizer(word_splitter=SpacyWordSplitter(pos_tags=True))
sent_tokens = tokenizer.tokenize("John refused to consider joining the club.")
# Emulate predications - for "refused" and "consider" and "joining"
predictions = [['B-ARG0', 'B-V', 'B-ARG1', 'I-ARG1', 'I-ARG1', 'I-ARG1', 'I-ARG1', 'O'],\
['B-ARG0', 'B-BV', 'I-BV', 'B-V', 'B-ARG1', 'I-ARG1', 'I-ARG1', 'O'],\
['B-ARG0', 'B-BV', 'I-BV', 'I-BV', 'B-V', 'B-ARG1', 'I-ARG1', 'O']]
# Consolidate
pred_dict = consolidate_predictions(predictions, sent_tokens)
# Check that only "refused to consider to join" is left
assert len(pred_dict) == 1
tags = list(pred_dict.values())[0]
assert get_predicate_text(sent_tokens, tags) == "refused to consider joining"
def test_predicate_consolidation(self):
"""
Test whether the predictor can correctly consolidate multiword
predicates.
"""
tokenizer = WordTokenizer(word_splitter=SpacyWordSplitter(pos_tags=True))
sent_tokens = tokenizer.tokenize("In December, John decided to join the party.")
# Emulate predications - for both "decided" and "join"
predictions = [['B-ARG2', 'I-ARG2', 'O', 'B-ARG0', 'B-V', 'B-ARG1', 'I-ARG1', \
'I-ARG1', 'I-ARG1', 'O'],
['O', 'O', 'O', 'B-ARG0', 'B-BV', 'I-BV', 'B-V', 'B-ARG1', \
'I-ARG1', 'O']]
# Consolidate
pred_dict = consolidate_predictions(predictions, sent_tokens)
# Check that only "decided to join" is left
assert len(pred_dict) == 1
tags = list(pred_dict.values())[0]
assert get_predicate_text(sent_tokens, tags) == "decided to join"
def format_extractions(sent_tokens, sent_predictions):
"""
Convert token-level raw predictions to clean extractions.
"""
# Consolidate predictions
if not (len(set(map(len, sent_predictions))) == 1):
raise AssertionError
assert len(sent_tokens) == len(sent_predictions[0])
pred_dict = consolidate_predictions(sent_predictions, sent_tokens)
# Build and return output dictionary
frames = []
for tags in pred_dict.values():
# Join multi-word predicates
tags = join_mwp(tags)
# Create description text
oie_frame = get_oie_frame(sent_tokens, tags)
frames.append(oie_frame)
return frames
