def build_full_link_set_dictionary(in_tokenized_sentences, in_language = DEFAULT_LANGUAGE):
stopped_sentences = common.stop(in_tokenized_sentences, in_language)
full_link_set = link_lexemes.extract_full_link_set(stopped_sentences, True)
result = collections.defaultdict(lambda: 0)
for lexeme in full_link_set:
result[lexeme[0]] = lexeme[1]
return result
def setUp(self):
raw_text = ' '.join([line.strip() for line in open(ExistenceAreasCalculationTest.INPUT_FILE)])
self.sentences = stop(tokenize_sentences(raw_text))
self.fls = make_lexemes_dict(link_lexemes.extract_full_link_set(self.sentences))
self.indexed_text = build_text_index(self.sentences, self.fls)
self.lexeme_powers = {id: lexeme_associative_power(id, self.indexed_text) \
for id in self.fls}
def setUp(self):
raw_text = ' '.join([line.strip() for line in open(ExistenceAreasCalculationTest.INPUT_FILE)])
self.sentences = stop(tokenize_sentences(raw_text))
self.fls = make_lexemes_dict(link_lexemes.extract_full_link_set(self.sentences))
self.indexed_text = build_text_index(self.sentences, self.fls)
existence_areas = calculate_existence_areas(self.indexed_text, self.fls)
self.const_phrases = extract_constant_phrases(existence_areas)
def initial_preprocessing(self, in_sentences):
# just words in the right order
self.fls = link_lexemes.extract_full_link_set(in_sentences,
self.language,
keep_positions = True)
self.pos_to_lex_mapping = {record[1]:record[0] for record in self.fls}
self.lex_to_pos_mapping = {record[0]:record[1] for record in self.fls}
# representing sentences as lists of FLS indices, not words themselves
self.indexed_text = self.build_text_index(in_sentences, self.lex_to_pos_mapping)
def initial_preprocessing(self, in_sentences):
# just words in the right order
self.fls = link_lexemes.extract_full_link_set(in_sentences,
self.language,
keep_positions=True)
self.pos_to_lex_mapping = {record[1]: record[0] for record in self.fls}
self.lex_to_pos_mapping = {record[0]: record[1] for record in self.fls}
# representing sentences as lists of FLS indices, not words themselves
self.indexed_text = self.build_text_index(in_sentences,
self.lex_to_pos_mapping)
def setUp(self):
raw_text = ' '.join([
line.strip()
for line in open(ExistenceAreasCalculationTest.INPUT_FILE)
])
self.sentences = stop(tokenize_sentences(raw_text))
self.fls = make_lexemes_dict(
link_lexemes.extract_full_link_set(self.sentences))
self.indexed_text = build_text_index(self.sentences, self.fls)
self.lexeme_powers = {id: lexeme_associative_power(id, self.indexed_text) \
for id in self.fls}
def setUp(self):
raw_text = ' '.join([
line.strip()
for line in open(ExistenceAreasCalculationTest.INPUT_FILE)
])
self.sentences = stop(tokenize_sentences(raw_text))
self.fls = make_lexemes_dict(
link_lexemes.extract_full_link_set(self.sentences))
self.indexed_text = build_text_index(self.sentences, self.fls)
existence_areas = calculate_existence_areas(self.indexed_text,
self.fls)
self.const_phrases = extract_constant_phrases(existence_areas)
def test_extraction(self):
fls = make_lexemes_dict(
link_lexemes.extract_full_link_set(self.sentences))
indexed_text = build_text_index(self.sentences, fls)
self.assertEqual(self.get_existence_areas(indexed_text, fls),
self.answer)
def test_extraction(self):
self.assertEqual(
link_lexemes.extract_full_link_set(self.sentences, True),
self.answer)
def test_extraction(self):
fls = make_lexemes_dict(link_lexemes.extract_full_link_set(self.sentences))
indexed_text = build_text_index(self.sentences, fls)
self.assertEqual(self.get_existence_areas(indexed_text, fls), self.answer)
def test_extraction(self):
self.assertEqual(link_lexemes.extract_full_link_set(self.sentences, True), self.answer)
