diff --git a/hadoop-mapreduce-project/CHANGES.txt b/hadoop-mapreduce-project/CHANGES.txt
index 55d2442e5a2..bdbdc22ad16 100644
--- a/hadoop-mapreduce-project/CHANGES.txt
+++ b/hadoop-mapreduce-project/CHANGES.txt
@@ -692,6 +692,9 @@ Release 2.7.3 - UNRELEASED
     MAPREDUCE-6549. multibyte delimiters with LineRecordReader cause
     duplicate records (wilfreds via rkanter)
 
+    MAPREDUCE-6583. Clarify confusing sentence in MapReduce tutorial document.
+    (Kai Sasaki via aajisaka)
+
 Release 2.7.2 - UNRELEASED
 
   INCOMPATIBLE CHANGES
diff --git a/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/MapReduceTutorial.md b/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/MapReduceTutorial.md
index e2aaaf6687c..74c6c66d5e8 100644
--- a/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/MapReduceTutorial.md
+++ b/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/MapReduceTutorial.md
@@ -311,7 +311,7 @@ public void reduce(Text key, Iterable<IntWritable> values,
 }
 ```
 
-The `Reducer` implementation, via the `reduce` method just sums up the values, which are the occurence counts for each key (i.e. words in this example).
+The `Reducer` implementation, via the `reduce` method just sums up the values, which are the occurrence counts for each key (i.e. words in this example).
 
 Thus the output of the job is:
 
@@ -348,7 +348,7 @@ Maps are the individual tasks that transform input records into intermediate rec
 
 The Hadoop MapReduce framework spawns one map task for each `InputSplit` generated by the `InputFormat` for the job.
 
-Overall, `Mapper` implementations are passed the `Job` for the job via the [Job.setMapperClass(Class)](../../api/org/apache/hadoop/mapreduce/Job.html) method. The framework then calls [map(WritableComparable, Writable, Context)](../../api/org/apache/hadoop/mapreduce/Mapper.html) for each key/value pair in the `InputSplit` for that task. Applications can then override the `cleanup(Context)` method to perform any required cleanup.
+Overall, mapper implementations are passed to the job via [Job.setMapperClass(Class)](../../api/org/apache/hadoop/mapreduce/Job.html) method. The framework then calls [map(WritableComparable, Writable, Context)](../../api/org/apache/hadoop/mapreduce/Mapper.html) for each key/value pair in the `InputSplit` for that task. Applications can then override the `cleanup(Context)` method to perform any required cleanup.
 
 Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to context.write(WritableComparable, Writable).
 
@@ -848,7 +848,7 @@ In the following sections we discuss how to submit a debug script with a job. Th
 
 ##### How to distribute the script file:
 
-The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* thescript file.
+The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* to the script file.
 
 ##### How to submit the script: