diff --git a/wordfreq_builder/wordfreq_builder/ninja.py b/wordfreq_builder/wordfreq_builder/ninja.py
index fa937cd..84c1818 100644
--- a/wordfreq_builder/wordfreq_builder/ninja.py
+++ b/wordfreq_builder/wordfreq_builder/ninja.py
@@ -123,7 +123,6 @@ def google_books_deps(dirname_in):
 
 def twitter_deps(input_filename, slice_prefix, combined_prefix, slices,
                  languages):
-
     lines = []
 
     slice_files = ['{prefix}.part{num:0>2d}'.format(prefix=slice_prefix,
diff --git a/wordfreq_builder/wordfreq_builder/tokenizers.py b/wordfreq_builder/wordfreq_builder/tokenizers.py
index 5815292..8e9f192 100644
--- a/wordfreq_builder/wordfreq_builder/tokenizers.py
+++ b/wordfreq_builder/wordfreq_builder/tokenizers.py
@@ -11,7 +11,10 @@ CLD2_BAD_CHAR_RANGE = "[%s]" % "".join(
         '\x0e-\x1f',
         '\x7f-\x9f',
         '\ud800-\udfff',
-        '\ufdd0-\ufdef'
+        '\ufdd0-\ufdef',
+        '\N{HANGUL FILLER}',
+        '\N{HANGUL CHOSEONG FILLER}',
+        '\N{HANGUL JUNGSEONG FILLER}'
     ] +
     [chr(65534+65536*x+y) for x in range(17) for y in range(2)]
 )
@@ -45,7 +48,7 @@ def cld2_detect_language(text):
     #       Language code: str
     #       Percent of text in this language: float
     #       Confidence score: float))
-    
+
     text = CLD2_BAD_CHARS_RE.sub('', text)
     return pycld2.detect(text)[2][0][1]