diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5460ffc..2fb7188 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,3 +1,18 @@
+## Version 2.5.1 (2021-09-02)
+
+- Import ftfy and use its `uncurl_quotes` method to turn curly quotes into
+  straight ones, providing consistency with multiple forms of apostrophes.
+
+- Set minimum version requierements on `regex`, `jieba`, and `langcodes`
+  so that tokenization will give consistent results.
+
+- Workaround an inconsistency in the `msgpack` API around
+  `strict_map_key=False`.
+
+## Version 2.5 (2021-04-15)
+
+- Incorporate data from the OSCAR corpus.
+
 ## Version 2.4.2 (2021-02-19)
 
 - When tokenizing Japanese or Korean, MeCab's dictionaries no longer have to
diff --git a/setup.py b/setup.py
index 8aa3f64..b938402 100755
--- a/setup.py
+++ b/setup.py
@@ -28,12 +28,20 @@ README_contents = open(os.path.join(current_dir, 'README.md'),
                        encoding='utf-8').read()
 doclines = README_contents.split("\n")
 dependencies = [
+<<<<<<< HEAD
     'msgpack >= 1.0', 'langcodes >= 3.0', 'regex >= 2020.04.04', 'ftfy >= 3.0'
+=======
+    'msgpack >= 1.0', 'langcodes >= 2.1', 'regex >= 2020.04.04'
+>>>>>>> origin/apostrophe-consistency
 ]
 
 setup(
     name="wordfreq",
+<<<<<<< HEAD
     version='2.5.1',
+=======
+    version='2.3.3',
+>>>>>>> origin/apostrophe-consistency
     maintainer='Robyn Speer',
     maintainer_email='rspeer@arborelia.net',
     url='http://github.com/LuminosoInsight/wordfreq/',
diff --git a/tests/test_french_and_related.py b/tests/test_apostrophes.py
similarity index 70%
rename from tests/test_french_and_related.py
rename to tests/test_apostrophes.py
index c27ecae..0c6b9b7 100644
--- a/tests/test_french_and_related.py
+++ b/tests/test_apostrophes.py
@@ -9,11 +9,24 @@ def test_apostrophes():
     assert tokenize("langues d'oïl", 'fr', include_punctuation=True) == ['langues', "d'", 'oïl']
     assert tokenize("l'heure", 'fr') == ['l', 'heure']
     assert tokenize("l'ànima", 'ca') == ['l', 'ànima']
+    assert tokenize("l'anima", 'it') == ['l', 'anima']
     assert tokenize("l'heure", 'fr', include_punctuation=True) == ["l'", 'heure']
     assert tokenize("L'Hôpital", 'fr', include_punctuation=True) == ["l'", 'hôpital']
     assert tokenize("aujourd'hui", 'fr') == ["aujourd'hui"]
     assert tokenize("This isn't French", 'en') == ['this', "isn't", 'french']
 
+    # This next behavior is not ideal -- we would prefer "dell'" to be handled
+    # the same as "l'" -- but this is the most consistent result we can get without
+    # Italian-specific rules.
+    #
+    # Versions of regex from 2019 and earlier would give ['dell', 'anima'], which
+    # is better but inconsistent.
+    assert tokenize("dell'anima", 'it') == ["dell'anima"]
+
+    # Versions of regex from 2019 and earlier would give ['hawai', 'i'], and that's
+    # an example of why we don't want the apostrophe-vowel fix to apply everywhere.
+    assert tokenize("hawai'i", 'en') == ["hawai'i"]
+
 
 def test_catastrophes():
     # More apostrophes, but this time they're in Catalan, and there's other
diff --git a/tests/test_chinese.py b/tests/test_chinese.py
index 4bde6c2..a6ff202 100644
--- a/tests/test_chinese.py
+++ b/tests/test_chinese.py
@@ -87,3 +87,14 @@ def test_unreasonably_long():
     assert word_frequency(lots_of_ls, 'zh') == 0.
     assert zipf_frequency(lots_of_ls, 'zh') == 0.
 
+
+def test_hyphens():
+    # An edge case of Chinese tokenization that changed sometime around
+    # jieba 0.42.
+
+    tok = tokenize('--------', 'zh', include_punctuation=True)
+    assert tok == ['-'] * 8
+    
+    tok = tokenize('--------', 'zh', include_punctuation=True, external_wordlist=True)
+    assert tok == ['--------']
+
diff --git a/wordfreq/chinese.py b/wordfreq/chinese.py
index 61e931a..95b6b5d 100644
--- a/wordfreq/chinese.py
+++ b/wordfreq/chinese.py
@@ -6,7 +6,11 @@ import gzip
 DICT_FILENAME = resource_filename('wordfreq', 'data/jieba_zh.txt')
 ORIG_DICT_FILENAME = resource_filename('wordfreq', 'data/jieba_zh_orig.txt')
 SIMP_MAP_FILENAME = resource_filename('wordfreq', 'data/_chinese_mapping.msgpack.gz')
-SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False, strict_map_key=False)
+try:
+    SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False, strict_map_key=False)
+except TypeError:
+    # work around incompatibility between pure-Python msgpack and C msgpack
+    SIMPLIFIED_MAP = msgpack.load(gzip.open(SIMP_MAP_FILENAME), raw=False)
 jieba_tokenizer = None
 jieba_orig_tokenizer = None
 
diff --git a/wordfreq/language_info.py b/wordfreq/language_info.py
index 3b736be..73a7b69 100644
--- a/wordfreq/language_info.py
+++ b/wordfreq/language_info.py
@@ -1,5 +1,5 @@
 from functools import lru_cache
-from langcodes import Language, best_match
+from langcodes import Language, closest_match
 
 
 # Text in scripts written without spaces has to be handled specially in our
@@ -45,7 +45,7 @@ EXTRA_JAPANESE_CHARACTERS = 'ー々〻〆'
 # happens in ConceptNet.
 
 
-def _language_in_list(language, targets, min_score=80):
+def _language_in_list(language, targets, max_distance=10):
     """
     A helper function to determine whether this language matches one of the
     target languages, with a match score above a certain threshold.
@@ -53,8 +53,8 @@ def _language_in_list(language, targets, min_score=80):
     The languages can be given as strings (language tags) or as Language
     objects. `targets` can be any iterable of such languages.
     """
-    matched = best_match(language, targets, min_score=min_score)
-    return matched[1] > 0
+    matched = closest_match(language, targets, max_distance=max_distance)
+    return matched[0] != 'und'
 
 
 @lru_cache(maxsize=None)