def get_prepared_KB(file_prepared_KB, stemmed=True):
phrase2candidates = {}
with open(file_prepared_KB, 'r', encoding='utf-8') as f:
p = ''
candi_list = []
for line in f:
if line.startswith('=='):
# process last one
if p != '':
phrase2candidates[p] = copy(candi_list)
# process next one
strs = line.split('\t')
p = strs[1].strip()
candi_list.clear() # clear the list
else:
strs = line.split('\t')
if len(strs) > 1:
explain = strs[1].replace("==SS==", "")
explain = explain.replace("==DB==", "")
if stemmed:
explain = stem_words(explain)
candi_list.append(explain)
if p != '':
phrase2candidates[p] = copy(candi_list)
# print(phrase2candidates)
return phrase2candidates
def get_meals(tokenized_string, enum=False):
"""
Returns a tuple of (index, meal) or a list of meals from a
tokenized string.
>>> raw_input_string = "I want cats for breakfast and dogs for dinner."
>>> tokenizer = nltk.WordPunctTokenizer()
>>> tokenized_string = tokenizer.tokenize(raw_input_string)
>>> for i,w in get_meals(tokenized_string, enum=True): print i,w
4 breakfast
8 dinner
"""
stemmed_string = utils.stem_words(tokenized_string)
stemmed_meals = utils.stem_words(wordlists.meal_types)
results = _extract_words_from_list(stemmed_meals, stemmed_string, True)
if enum:
return [(i, tokenized_string[i]) for i, w in results]
else:
return [tokenized_string[i] for i, w in results]
def get_meals(tokenized_string, enum=False):
"""
Returns a tuple of (index, meal) or a list of meals from a
tokenized string.
>>> raw_input_string = "I want cats for breakfast and dogs for dinner."
>>> tokenizer = nltk.WordPunctTokenizer()
>>> tokenized_string = tokenizer.tokenize(raw_input_string)
>>> for i,w in get_meals(tokenized_string, enum=True): print i,w
4 breakfast
8 dinner
"""
stemmed_string = utils.stem_words(tokenized_string)
stemmed_meals = utils.stem_words(wordlists.meal_types)
results = extract_words_from_list(stemmed_meals, stemmed_string, True)
if enum:
return [(i, tokenized_string[i]) for i, w in results]
else:
return [tokenized_string[i] for i, w in results]
def get_cuisines(tokenized_string, enum=False):
"""
Returns a tuple of (index, cuisine) or a list of cuisines from a
tokenized string.
>>> raw_input_string = "I want a chinese or mexican dish."
>>> tokenizer = nltk.WordPunctTokenizer()
>>> tokenized_string = tokenizer.tokenize(raw_input_string)
>>> for i,w in get_cuisines(tokenized_string, enum=True): print i,w
3 chinese
5 mexican
"""
stemmed_string = utils.stem_words(tokenized_string)
cuisines = set.difference(wordlists.cuisines, wordlists.meal_types)
cuisines = cuisines.union(wordlists.list_of_adjectivals)
stemmed_cuisines = utils.stem_words(cuisines)
results = _extract_words_from_list(stemmed_cuisines, stemmed_string, True)
if enum:
return [(i, tokenized_string[i]) for i, w in results]
else:
return [tokenized_string[i] for i, w in results]
def get_cuisines(tokenized_string, enum=False):
"""
Returns a tuple of (index, cuisine) or a list of cuisines from a
tokenized string.
>>> raw_input_string = "I want a chinese or mexican dish."
>>> tokenizer = nltk.WordPunctTokenizer()
>>> tokenized_string = tokenizer.tokenize(raw_input_string)
>>> for i,w in get_cuisines(tokenized_string, enum=True): print i,w
3 chinese
5 mexican
"""
stemmed_string = utils.stem_words(tokenized_string)
cuisines = set.difference(wordlists.cuisines, wordlists.meal_types)
cuisines = cuisines.union(wordlists.list_of_adjectivals)
stemmed_cuisines = utils.stem_words(cuisines)
results = extract_words_from_list(stemmed_cuisines, stemmed_string, True)
if enum:
return [(i, tokenized_string[i]) for i, w in results]
else:
return [tokenized_string[i] for i, w in results]
def filter_text(self):
text = self.get_tweet_text()
text = set(utils.tokenise(text))
filteredText = list(self.remove_stopwords(text))
keywords = utils.stem_words(filteredText)
self.set_tweet_tokens(keywords)
