diff --git a/data-scripts/count_wikipedia.py b/data-scripts/count_wikipedia.py
old mode 100755
new mode 100644
index aacf8d0f..348c1c38
--- a/data-scripts/count_wikipedia.py
+++ b/data-scripts/count_wikipedia.py
@@ -3,16 +3,18 @@
 import sys
 import os
 import re
-import codecs
 import operator
 import datetime
-import nltk
 import warnings
+import multiprocessing
+import time
+import io
 
+import nltk
 from unidecode import unidecode
 
 def usage():
-    print '''
+    print('''
 tokenize a directory of text and count unigrams.
 
 usage:
@@ -48,14 +50,16 @@ def usage():
 Then run:
 ./WikiExtractor.py -o en_sents --no-templates enwiki-20151002-pages-articles.xml.bz2
 
-''' % sys.argv[0]
+''' % sys.argv[0])
 
-SENTENCES_PER_BATCH = 500000 # after each batch, delete all counts with count == 1 (hapax legomena)
-PRE_SORT_CUTOFF = 300        # before sorting, discard all words with less than this count
+
+SENTENCES_PER_BATCH = 500000  # after each batch, delete all counts with count == 1 (hapax legomena)
+PRE_SORT_CUTOFF = 300         # before sorting, discard all words with less than this count
 
 ALL_NON_ALPHA = re.compile(r'^[\W\d]*$', re.UNICODE)
 SOME_NON_ALPHA = re.compile(r'[\W\d]', re.UNICODE)
 
+
 class TopTokenCounter(object):
     def __init__(self):
         self.count = {}
@@ -110,7 +114,7 @@ def batch_prune(self):
 
     def pre_sort_prune(self):
         under_cutoff = set()
-        for token, count in self.count.iteritems():
+        for token, count in self.count.items():
             if count < PRE_SORT_CUTOFF:
                 under_cutoff.add(token)
         for token in under_cutoff:
@@ -127,43 +131,81 @@ def get_stats(self):
         ts = self.get_ts()
         return "%s keys(count): %d" % (ts, len(self.count))
 
+    def merge(self, other):
+        self.discarded |= other.discarded
+        self.legomena ^= other.legomena
+        for token, num in other.count.items():
+            if token in self.count:
+                self.count[token] += num
+            else:
+                self.count[token] = num
+
+
+def count_file(path):
+    """
+    Scan the file at given path, tokenize all lines and return the filled TopTokenCounter
+    and the number of processed lines.
+    """
+    counter = TopTokenCounter()
+    lines = 0
+    for line in io.open(path, 'r', encoding='utf8'):
+        with warnings.catch_warnings():
+            # unidecode() occasionally (rarely but enough to clog terminal outout)
+            # complains about surrogate characters in some wikipedia sentences.
+            # ignore those warnings.
+            warnings.simplefilter('ignore')
+            line = unidecode(line)
+        tokens = nltk.word_tokenize(line)
+        counter.add_tokens(tokens)
+        lines += 1
+    return counter, lines
+
+
 def main(input_dir_str, output_filename):
     counter = TopTokenCounter()
-    print counter.get_ts(), 'starting...'
+    print(counter.get_ts(), 'starting...')
+    tic = time.time()
+    pruned_lines = 0
     lines = 0
-    for root, dirs, files in os.walk(input_dir_str, topdown=True):
-        if not files:
-            continue
-        for fname in files:
-            path = os.path.join(root, fname)
-            for line in codecs.open(path, 'r', 'utf8'):
-                with warnings.catch_warnings():
-                    # unidecode() occasionally (rarely but enough to clog terminal outout)
-                    # complains about surrogate characters in some wikipedia sentences.
-                    # ignore those warnings.
-                    warnings.simplefilter('ignore')
-                    line = unidecode(line)
-                tokens = nltk.word_tokenize(line)
-                counter.add_tokens(tokens)
-                lines += 1
-                if lines % SENTENCES_PER_BATCH == 0:
-                    counter.batch_prune()
-                    print counter.get_stats()
-                    print 'processing: %s' % path
-    print counter.get_stats()
-    print 'deleting tokens under cutoff of', PRE_SORT_CUTOFF
+    files = 0
+    process_pool = multiprocessing.Pool()
+    # Some python iterator magic: Pool.imap() maps the given function over the iterable
+    # using the process pool. The iterable is produced by creating the full path of every
+    # file in every directory (thus, the nested generator expression).
+    for fcounter, l in process_pool.imap(
+            count_file, (os.path.join(root, fname)
+                         for root, dirs, files in os.walk(input_dir_str, topdown=True)
+                         if files
+                         for fname in files), 4):
+        lines += l
+        files += 1
+        counter.merge(fcounter)
+        if (lines - pruned_lines) >= SENTENCES_PER_BATCH:
+            counter.batch_prune()
+            pruned_lines = lines
+            print(counter.get_stats())
+
+    toc = time.time()
+    print("Finished reading input data. Read %d files with %d lines in %.2fs."
+          % (files, lines, toc-tic))
+    print(counter.get_stats())
+
+    print('deleting tokens under cutoff of', PRE_SORT_CUTOFF)
     counter.pre_sort_prune()
-    print 'done'
-    print counter.get_stats()
-    print counter.get_ts(), 'sorting...'
+    print('done')
+    print(counter.get_stats())
+
+    print(counter.get_ts(), 'sorting...')
     sorted_pairs = counter.get_sorted_pairs()
-    print counter.get_ts(), 'done'
-    print 'writing...'
-    with codecs.open(output_filename, 'w', 'utf8') as f:
+    print(counter.get_ts(), 'done')
+
+    print('writing...')
+    with io.open(output_filename, 'w', encoding='utf8') as f:
         for token, count in sorted_pairs:
             f.write('%-18s %d\n' % (token, count))
     sys.exit(0)
 
+
 if __name__ == '__main__':
     if len(sys.argv) != 3:
         usage()