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"""
Fills out message meta and frame attributes.
"""
tokenized_string = g.generate_tokenized_string(raw_input_string)
parseTree = g.generate_stanford_parse_tree(raw_input_string)
subjects = extract_subject_nodes(parseTree)
if subjects:
self.frame['subject'] = [get_node_string(subject)
for subject in subjects]
words_temporary_pos = extract_close_keywords(
PreferenceMessage.keywords_temporary_pos,
tokenized_string,
2)
words_temporary_neg = extract_close_keywords(
PreferenceMessage.keywords_temporary_neg,
tokenized_string,
2)
words_permanent_pos = extract_close_keywords(
PreferenceMessage.keywords_permanent_pos,
tokenized_string,
2)
words_permanent_neg = extract_close_keywords(
PreferenceMessage.keywords_permanent_neg,
tokenized_string,
2)
words_temporary = words_temporary_pos + words_temporary_neg
words_permanent = words_permanent_pos + words_permanent_neg
if words_temporary and words_permanent:
# Confused
# self.frame['temporal'] = None
# self.frame['word'] = None
self.frame['subject'] = [get_node_string(subject)
for subject in subjects]
words_temporary_pos = extract_close_keywords(
PreferenceMessage.keywords_temporary_pos,
tokenized_string,
2)
words_temporary_neg = extract_close_keywords(
PreferenceMessage.keywords_temporary_neg,
tokenized_string,
2)
words_permanent_pos = extract_close_keywords(
PreferenceMessage.keywords_permanent_pos,
tokenized_string,
2)
words_permanent_neg = extract_close_keywords(
PreferenceMessage.keywords_permanent_neg,
tokenized_string,
2)
words_temporary = words_temporary_pos + words_temporary_neg
words_permanent = words_permanent_pos + words_permanent_neg
if words_temporary and words_permanent:
# Confused
# self.frame['temporal'] = None
# self.frame['word'] = None
# This check is skipped due to an error in not using the POS
# when looking up synsets.
# TODO: Fix (example: fish)
pass
if words_temporary:
self.frame['temporal'] = 'temporary'
self.frame['word'] = words_temporary[0]
else: # words_permanent
def _parse(self, raw_input_string, g):
"""
Fills out message meta and frame attributes.
"""
tokenized_string = g.generate_tokenized_string(raw_input_string)
parseTree = g.generate_stanford_parse_tree(raw_input_string)
subjects = extract_subject_nodes(parseTree)
if subjects:
self.frame['subject'] = [get_node_string(subject)
for subject in subjects]
words_temporary_pos = extract_close_keywords(
PreferenceMessage.keywords_temporary_pos,
tokenized_string,
2)
words_temporary_neg = extract_close_keywords(
PreferenceMessage.keywords_temporary_neg,
tokenized_string,
2)
words_permanent_pos = extract_close_keywords(
PreferenceMessage.keywords_permanent_pos,
tokenized_string,
2)
words_permanent_neg = extract_close_keywords(
PreferenceMessage.keywords_permanent_neg,
tokenized_string,
2)
words_temporary = words_temporary_pos + words_temporary_neg
words_permanent = words_permanent_pos + words_permanent_neg
parseTree = g.generate_stanford_parse_tree(raw_input_string)
subjects = extract_subject_nodes(parseTree)
if subjects:
self.frame['subject'] = [get_node_string(subject)
for subject in subjects]
words_temporary_pos = extract_close_keywords(
PreferenceMessage.keywords_temporary_pos,
tokenized_string,
2)
words_temporary_neg = extract_close_keywords(
PreferenceMessage.keywords_temporary_neg,
tokenized_string,
2)
words_permanent_pos = extract_close_keywords(
PreferenceMessage.keywords_permanent_pos,
tokenized_string,
2)
words_permanent_neg = extract_close_keywords(
PreferenceMessage.keywords_permanent_neg,
tokenized_string,
2)
words_temporary = words_temporary_pos + words_temporary_neg
words_permanent = words_permanent_pos + words_permanent_neg
if words_temporary and words_permanent:
# Confused
# self.frame['temporal'] = None
# self.frame['word'] = None
# This check is skipped due to an error in not using the POS
# when looking up synsets.
# TODO: Fix (example: fish)
pass
def confidence(raw_input_string, generators):
return get_keyword_confidence(raw_input_string,
PreferenceMessage.keywords,
3)