diff --git a/ngrams/term_frequencies.py b/ngrams/term_frequencies.py
index 21828a7..38de903 100755
--- a/ngrams/term_frequencies.py
+++ b/ngrams/term_frequencies.py
@@ -30,7 +30,7 @@ def remove_punct(sentence):
             new_sentence.append(new_token)
     return new_sentence
 
-def my_tokenizer(text, mwe_pass, mwe_tokenize, stopWords):
+def my_tokenizer(text, mwe_pass, mwe_tokenize, stopWords, ngram_output):
     # remove stopwords, punctuation, urls, lower case
     # lowercase        
     if text is None:
@@ -58,7 +58,6 @@ def my_tokenizer(text, mwe_pass, mwe_tokenize, stopWords):
     # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
     # here we take a 10 percent sample of sentences 
     if mwe_pass == 'first':
-        ngram_output = partition.replace("parquet","txt")
         sentences = list(sentences)
         for sentence in sentences:
             if random() <= 0.1:
@@ -79,13 +78,13 @@ def my_tokenizer(text, mwe_pass, mwe_tokenize, stopWords):
                 yield token
 
 
-def tf_comments(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords):
+def tf_comments(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords, ngram_output):
     for key, posts in subreddit_weeks:
         subreddit, week = key
         tfs = Counter([])
         authors = Counter([])
         for post in posts:
-            tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords)
+            tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords, ngram_output)
             tfs.update(tokens)
             authors.update([post.author])
 
@@ -95,16 +94,16 @@ def tf_comments(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords):
         for author, tf in authors.items():
             yield [False, subreddit, author, week, tf]
 
-def tf_posts(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords):
+def tf_posts(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords, ngram_output):
     for key, posts in subreddit_weeks:
         subreddit, week = key
         tfs = Counter([])
         authors = Counter([])
         for post in posts:
-            title_tokens = my_tokenizer(post.title, mwe_pass, mwe_tokenize, stopWords)
+            title_tokens = my_tokenizer(post.title, mwe_pass, mwe_tokenize, stopWords, ngram_output)
             tfs.update(title_tokens)
             if post.selftext is not None and post.selftext != "":
-                selftext_tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords)
+                selftext_tokens = my_tokenizer(post.selftext, mwe_pass, mwe_tokenize, stopWords, ngram_output)
                 tfs.update(selftext_tokens)
             authors.update([post.author])
 
@@ -194,7 +193,7 @@ def weekly_tf(partition,
 
     # we follow the approach described in datta, phelan, adar 2017
 
-    outrows = tf_func(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords)
+    outrows = tf_func(subreddit_weeks, mwe_pass, mwe_tokenize, stopWords, ngram_output)
 
     outchunksize = 100000