def extract_junction(parse_tree, word):
"""
Returns either 'and' or 'or'. Defaults to 'and' if junction cannot be
determined.
>>> import nltk
>>> raw_input_string = "What can I make with carrots and children or celery?"
>>> tokenizer = nltk.WordPunctTokenizer()
>>> tokenized_string = tokenizer.tokenize(raw_input_string)
>>> tree = get_parse_tree(tokenized_string)
>>> extract_junction(tree, 'carrots')
'and'
>>> extract_junction(tree, 'children')
'and'
>>> extract_junction(tree, 'celery')
'or'
>>> extract_junction(tree, 'rats') == None
True
"""
# locate the word node
for node in parse_tree.getLeaves():
if node.value() == word:
# extract the junction node
node = extract_junction_node(parse_tree, node)
# return the node junction type
if node:
nodeString = get_node_string(node)
if 'and' in nodeString:
return 'and'
elif 'or' in nodeString:
return 'or'
else:
return 'and'
return None
def extract_subjects(parse_tree, enum=True):
"""
Returns a list of subject words.
"""
for node in extract_subject_nodes(parse_tree):
word = get_node_string(node)
if enum:
yield (parse_tree.indexOf(node), word)
else:
yield word
def _parse(self, raw_input_string):
"""
Fills out message meta and frame attributes.
"""
tokenizer = nltk.WordPunctTokenizer()
tokenized_string = tokenizer.tokenize(raw_input_string)
subjects = extract_subject_nodes(tokenized_string)
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
if words_temporary:
self.frame['temporal'] = 'temporary'
self.frame['word'] = words_temporary[0]
else: # words_permanent
self.frame['temporal'] = 'permanent'
self.frame['word'] = words_permanent[0]
words_pos = words_temporary_pos + words_permanent_pos
words_neg = words_temporary_neg + words_permanent_neg
if words_pos and words_neg:
# Confused
self.frame['prefer'] = None
if words_pos:
self.frame['prefer'] = True
else: # words_neg
self.frame['prefer'] = False
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
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
self.frame['temporal'] = 'permanent'
self.frame['word'] = words_permanent[0]
words_pos = words_temporary_pos + words_permanent_pos
words_neg = words_temporary_neg + words_permanent_neg
if words_pos and words_neg:
# Confused
self.frame['prefer'] = None
if words_pos:
self.frame['prefer'] = True
else: # words_neg
self.frame['prefer'] = False
