diff --git a/naive-nlu/tree_nlu/parsing.py b/naive-nlu/tree_nlu/parsing.py
index 6e1fe30..ed5903a 100644
--- a/naive-nlu/tree_nlu/parsing.py
+++ b/naive-nlu/tree_nlu/parsing.py
@@ -88,7 +88,7 @@ def integrate_language(knowledge_base, example):
 
         for position, atom in lower_levels:
             logging.debug("\x1b[1mSelecting\x1b[0m: {}".format(atom))
-            similar = get_similar_tree(knowledge_base, atom)
+            similar = get_similar_tree(knowledge_base, atom, tokens)
             remix, (start_bounds, end_bounds) = build_remix_matrix(knowledge_base, tokens, atom, similar)
             _, matcher, result = make_template(knowledge_base, tokens, atom)
             logging.debug("Tx: {}".format(tokens))
@@ -138,7 +138,7 @@ def apply_remix(tokens, remix):
 def build_remix_matrix(knowledge_base, tokens, atom, similar):
     tokens = list(tokens)
     tokens, matcher, result = make_template(knowledge_base, tokens, atom)
-    similar_matcher, similar_result, similar_result_resolved, _ = similar
+    similar_matcher, similar_result, similar_result_resolved, _, _ = similar
 
     start_bounds, end_bounds = find_bounds(matcher, similar_matcher)
 
@@ -219,7 +219,7 @@ def find_bounds(matcher, similar_matcher):
     return start_bounds, end_bounds
 
 
-def get_similar_tree(knowledge_base, atom):
+def get_similar_tree(knowledge_base, atom, tokens):
     possibilities = []
 
     # Find matching possibilities
@@ -243,12 +243,17 @@ def get_similar_tree(knowledge_base, atom):
                     raw))
 
         # TODO: Probably should take into account the categories of the elements in the "intake" ([0]) element
-        score = sum([resolved[i] == atom[i]
+        atom_score = sum([resolved[i] == atom[i]
                      for i
                      in range(min(len(resolved),
                                   len(atom)))])
-        sorted_possibilities.append((raw, possibility, resolved, score))
-    sorted_possibilities = sorted(sorted_possibilities, key=lambda p: p[3], reverse=True)
+        token_score = sum([similar_token in tokens
+                           for similar_token
+                           in raw])
+
+        sorted_possibilities.append((raw, possibility, resolved, atom_score, token_score))
+
+    sorted_possibilities = sorted(sorted_possibilities, key=lambda p: p[3] * 100 + p[4], reverse=True)
     if len(sorted_possibilities) < 1:
         return None