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Merge remote-tracking branch 'origin/apostrophe-consistency'
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cc4f39d8c2
15
CHANGELOG.md
15
CHANGELOG.md
@ -1,3 +1,18 @@
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## Version 2.5.1 (2021-09-02)
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- Import ftfy and use its `uncurl_quotes` method to turn curly quotes into
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straight ones, providing consistency with multiple forms of apostrophes.
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- Set minimum version requierements on `regex`, `jieba`, and `langcodes`
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so that tokenization will give consistent results.
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- Workaround an inconsistency in the `msgpack` API around
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`strict_map_key=False`.
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## Version 2.5 (2021-04-15)
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- Incorporate data from the OSCAR corpus.
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## Version 2.4.2 (2021-02-19)
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- When tokenizing Japanese or Korean, MeCab's dictionaries no longer have to
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8
setup.py
8
setup.py
@ -28,12 +28,20 @@ README_contents = open(os.path.join(current_dir, 'README.md'),
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encoding='utf-8').read()
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doclines = README_contents.split("\n")
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dependencies = [
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<<<<<<< HEAD
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'msgpack >= 1.0', 'langcodes >= 3.0', 'regex >= 2020.04.04', 'ftfy >= 3.0'
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=======
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'msgpack >= 1.0', 'langcodes >= 2.1', 'regex >= 2020.04.04'
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>>>>>>> origin/apostrophe-consistency
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]
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setup(
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name="wordfreq",
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<<<<<<< HEAD
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version='2.5.1',
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=======
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version='2.3.3',
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>>>>>>> origin/apostrophe-consistency
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maintainer='Robyn Speer',
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maintainer_email='rspeer@arborelia.net',
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url='http://github.com/LuminosoInsight/wordfreq/',
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@ -9,11 +9,24 @@ def test_apostrophes():
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assert tokenize("langues d'oïl", 'fr', include_punctuation=True) == ['langues', "d'", 'oïl']
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assert tokenize("l'heure", 'fr') == ['l', 'heure']
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assert tokenize("l'ànima", 'ca') == ['l', 'ànima']
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assert tokenize("l'anima", 'it') == ['l', 'anima']
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assert tokenize("l'heure", 'fr', include_punctuation=True) == ["l'", 'heure']
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assert tokenize("L'Hôpital", 'fr', include_punctuation=True) == ["l'", 'hôpital']
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assert tokenize("aujourd'hui", 'fr') == ["aujourd'hui"]
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assert tokenize("This isn't French", 'en') == ['this', "isn't", 'french']
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# This next behavior is not ideal -- we would prefer "dell'" to be handled
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# the same as "l'" -- but this is the most consistent result we can get without
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# Italian-specific rules.
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#
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# Versions of regex from 2019 and earlier would give ['dell', 'anima'], which
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# is better but inconsistent.
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assert tokenize("dell'anima", 'it') == ["dell'anima"]
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# Versions of regex from 2019 and earlier would give ['hawai', 'i'], and that's
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# an example of why we don't want the apostrophe-vowel fix to apply everywhere.
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assert tokenize("hawai'i", 'en') == ["hawai'i"]
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def test_catastrophes():
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# More apostrophes, but this time they're in Catalan, and there's other
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@ -87,3 +87,14 @@ def test_unreasonably_long():
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assert word_frequency(lots_of_ls, 'zh') == 0.
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assert zipf_frequency(lots_of_ls, 'zh') == 0.
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def test_hyphens():
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# An edge case of Chinese tokenization that changed sometime around
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# jieba 0.42.
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tok = tokenize('--------', 'zh', include_punctuation=True)
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assert tok == ['-'] * 8
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tok = tokenize('--------', 'zh', include_punctuation=True, external_wordlist=True)
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assert tok == ['--------']
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@ -6,7 +6,11 @@ import gzip
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DICT_FILENAME = resource_filename('wordfreq', 'data/jieba_zh.txt')
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ORIG_DICT_FILENAME = resource_filename('wordfreq', 'data/jieba_zh_orig.txt')
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SIMP_MAP_FILENAME = resource_filename('wordfreq', 'data/_chinese_mapping.msgpack.gz')
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SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False, strict_map_key=False)
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try:
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SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False, strict_map_key=False)
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except TypeError:
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# work around incompatibility between pure-Python msgpack and C msgpack
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SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False)
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jieba_tokenizer = None
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jieba_orig_tokenizer = None
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@ -1,5 +1,5 @@
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from functools import lru_cache
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from langcodes import Language, best_match
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from langcodes import Language, closest_match
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# Text in scripts written without spaces has to be handled specially in our
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@ -45,7 +45,7 @@ EXTRA_JAPANESE_CHARACTERS = 'ー々〻〆'
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# happens in ConceptNet.
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def _language_in_list(language, targets, min_score=80):
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def _language_in_list(language, targets, max_distance=10):
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"""
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A helper function to determine whether this language matches one of the
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target languages, with a match score above a certain threshold.
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@ -53,8 +53,8 @@ def _language_in_list(language, targets, min_score=80):
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The languages can be given as strings (language tags) or as Language
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objects. `targets` can be any iterable of such languages.
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"""
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matched = best_match(language, targets, min_score=min_score)
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return matched[1] > 0
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matched = closest_match(language, targets, max_distance=max_distance)
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return matched[0] != 'und'
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@lru_cache(maxsize=None)
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