diff --git a/questions/110_evaluate-translation-quality-with-meteor-score/example.json b/questions/110_evaluate-translation-quality-with-meteor-score/example.json
index d1084515..26203c35 100644
--- a/questions/110_evaluate-translation-quality-with-meteor-score/example.json
+++ b/questions/110_evaluate-translation-quality-with-meteor-score/example.json
@@ -1,5 +1,5 @@
 {
   "input": "meteor_score('Rain falls gently from the sky', 'Gentle rain drops from the sky')",
   "output": "0.625",
-  "reasoning": "The function identifies 4 unigram matches ('rain', 'gently'/'gentle', 'from', 'sky'), computes precision (4/6) and recall (4/5), calculates an F-mean, and then apply a small penalty for two chunks."
+  "reasoning": "The function identifies 4 unigram matches ('rain', 'from', 'the', 'sky'), computes precision (4/6) and recall (4/6), calculates an F-mean, and then apply a small penalty for two chunks."
 }
diff --git a/questions/110_evaluate-translation-quality-with-meteor-score/learn.md b/questions/110_evaluate-translation-quality-with-meteor-score/learn.md
index 1a981bbd..992c97b7 100644
--- a/questions/110_evaluate-translation-quality-with-meteor-score/learn.md
+++ b/questions/110_evaluate-translation-quality-with-meteor-score/learn.md
@@ -66,10 +66,9 @@ F_mean = (Precision * Recall) /
 
 ### 5. Chunk Calculation
 - Contiguous matched sequences:
-  1. ['quick', 'brown', 'fox']
-  2. ['jumps', 'over']
-  3. ['lazy', 'dog']
-- Number of Chunks: 3
+  1. ['quick', 'brown', 'fox', jumps', 'over']
+  2. ['lazy', 'dog']
+- Number of Chunks: 2
 - Total Number of Unigram Matches: 7
 
 ### 6. Penalty Calculation (betta = 3, gamma = 0.5)