def extract_rule(conn, corpus_id, letter_sound_map, raw_pair):
rule_key = first(raw_pair)
value = second(raw_pair)
rule = None
if rule_key == 'rhyme': rule = dogma.RhymeRule(letter_sound_map.get(value))
elif rule_key == 'blank': rule = dogma.BlankRule()
elif rule_key == 'alliteration': rule = dogma.AlliterationRule(value)
elif rule_key == 'keyword': rule = dogma.KeywordRule(value, conn, corpus_id)
elif rule_key == 'fuzzy': rule = dogma.FuzzyKeywordRule(value, conn, corpus_id)
elif rule_key == 'syllables': rule = dogma.SyllableCountRule(value)
return rule
def words(sentence):
tagged_sentence = tag(sentence)
tagged_words = filter(lambda tu: match(word_tag_re, second(tu)), tagged_sentence)
ws = map(first, tagged_words)
return list(ws)
@lru_cache(maxsize=2056)
def words(sentence):
tagged_sentence = tag(sentence)
tagged_words = filter(lambda tu: match(word_tag_re, second(tu)),
tagged_sentence)
ws = map(first, tagged_words)
return list(ws)
def stem_sentence(sentence):
stemmed = map(stem_word, words(sentence))
return list(stemmed)
is_divider = lambda tu: DIVIDER_TAG == second(tu)
def split_multiclause(sentence, tagged_sentence):
# extract the text the divider tag represents
divider = first(find_first(is_divider, tagged_sentence))
if divider is not None:
first_clause = sentence[0:sentence.index(divider)].rstrip()
second_clause = sentence[sentence.index(divider) + 1:].lstrip()
return [first_clause, second_clause]
else:
return [sentence]
def expand_multiclauses(sentences):
# TODO consider itertools
tokenized_words = nltk.word_tokenize(sentence_string)
return nltk.pos_tag(tokenized_words)
word_tag_re = re.compile("^[A-Z]+$")
@lru_cache(maxsize=2056)
def words(sentence):
tagged_sentence = tag(sentence)
tagged_words = filter(lambda tu: match(word_tag_re, second(tu)), tagged_sentence)
ws = map(first, tagged_words)
return list(ws)
def stem_sentence(sentence):
stemmed = map(stem_word, words(sentence))
return list(stemmed)
is_divider = lambda tu: DIVIDER_TAG == second(tu)
def split_multiclause(sentence, tagged_sentence):
# extract the text the divider tag represents
divider = first(find_first(is_divider, tagged_sentence))
if divider is not None:
first_clause = sentence[0:sentence.index(divider)].rstrip()
second_clause = sentence[sentence.index(divider)+1:].lstrip()
return [first_clause, second_clause]
else:
return [sentence]
def expand_multiclauses(sentences):
# TODO consider itertools
split = []
for sentence in sentences:
