diff --git a/evaluate.py b/evaluate.py
index 699b894..e5e2835 100644
--- a/evaluate.py
+++ b/evaluate.py
@@ -20,8 +20,6 @@ for filename in os.listdir("data"):
     if os.path.isfile(f):
         data_files.append(pd.read_csv(f, sep='\t', header=None))
 
-recognized = 0
-unrecognized = 0
 true_positives = 0
 false_positives = 0
 false_negatives = 0
@@ -43,11 +41,6 @@ for df in data_files:
     entries_count = len(user_speeches)
 
     found_rules = user_speeches.apply(lambda x: grammar.find_matching_rules(decode_prompt(x)))
-    parsed = user_speeches.apply(lambda x: bool(grammar.find_matching_rules(decode_prompt(x))))
-    true_count = parsed.sum()
-    false_count = len(parsed) - true_count
-    recognized += true_count
-    unrecognized += false_count
     
     for line, rules in zip(df.iterrows(), found_rules):
         act = line[1]['act'].split('(')[0]
@@ -62,12 +55,10 @@ for df in data_files:
             false_negatives += 1
             acts_not_recognized[act] += 1
 
-accuracy = recognized / (recognized + unrecognized)
+accuracy = (true_positives + false_positives) / ((true_positives + false_positives) + sum([x for x in acts_not_recognized.values()]))
 precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) != 0 else 0
 recall = true_positives / (true_positives + false_negatives) if (true_positives + false_negatives) != 0 else 0
 
-print(f"Recognized user utterances: {recognized}")
-print(f"Unrecognized user utterances: {unrecognized}")
 print(f"Accuracy: {accuracy}")
 print(f"Precision: {precision}")
 print(f"Recall: {recall}")