wordfreq/tests/test_japanese.py
2018-04-26 15:53:07 -04:00

64 lines
2.5 KiB
Python

from nose.tools import eq_, assert_almost_equal
from wordfreq import tokenize, simple_tokenize, word_frequency
def test_tokens():
eq_(tokenize('おはようございます', 'ja'),
['おはよう', 'ござい', 'ます'])
def test_simple_tokenize():
# When Japanese is run through simple_tokenize -- either because it's
# tagged with the wrong language, or because we want to pass through
# Japanese text without getting MeCab involved -- it will be split at
# boundaries between Japanese and non-Japanese scripts, but all Japanese
# scripts will be stuck together. Here the switch between hiragana
# (ひらがな) and katakana (カタカナ) is not a boundary, but the switch
# between katakana and romaji is.
#
# We used to try to infer word boundaries between hiragana and katakana,
# but this leads to edge cases that are unsolvable without a dictionary.
ja_text = 'ひらがなカタカナromaji'
eq_(
simple_tokenize(ja_text),
['ひらがなカタカナ', 'romaji']
)
# An example that would be multiple tokens if tokenized as 'ja' via MeCab,
# but sticks together in simple_tokenize
eq_(simple_tokenize('おはようございます'), ['おはようございます'])
# Names that use the weird possessive marker ヶ, which is technically a
# katakana even though it's being used like a kanji, stay together as one
# token
eq_(simple_tokenize("犬ヶ島"), ["犬ヶ島"])
# The word in ConceptNet that made me notice that simple_tokenize used
# to have a problem with the character 々
eq_(simple_tokenize("晴々しい"), ["晴々しい"])
# Explicit word separators are still token boundaries, such as the dot
# between "toner" and "cartridge" in "toner cartridge"
eq_(simple_tokenize("トナー・カートリッジ"), ["トナー", "カートリッジ"])
# This word has multiple weird characters that aren't quite kanji in it,
# and is in the dictionary
eq_(simple_tokenize("見ヶ〆料"), ["見ヶ〆料"])
def test_combination():
ohayou_freq = word_frequency('おはよう', 'ja')
gozai_freq = word_frequency('ござい', 'ja')
masu_freq = word_frequency('ます', 'ja')
assert_almost_equal(
word_frequency('おはようおはよう', 'ja'),
ohayou_freq / 2
)
assert_almost_equal(
1.0 / word_frequency('おはようございます', 'ja'),
1.0 / ohayou_freq + 1.0 / gozai_freq + 1.0 / masu_freq
)