diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
index caa8f7bb55094..68d919da579fb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
@@ -29,6 +29,10 @@ import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
  * values in the input array are ignored.
  * It returns an array of n-grams where each n-gram is represented by a space-separated string of
  * words.
+ *
+ * When the input is empty, an empty array is returned.
+ * When the input array length is less than n (number of elements per n-gram), a single n-gram
+ * consisting of the input array is returned.
  */
 @Experimental
 class NGram(override val uid: String)
@@ -38,28 +42,27 @@ class NGram(override val uid: String)
 
   /**
    * Minimum n-gram length, >= 1.
-   * Defauult: 2, bigram features
+   * Default: 2, bigram features
    * @group param
    */
-  val NGramLength: IntParam = new IntParam(this, "NGramLength", "number elements per n-gram (>=1)",
+  val n: IntParam = new IntParam(this, "n", "number elements per n-gram (>=1)",
     ParamValidators.gtEq(1))
 
   /** @group setParam */
-  def setNGramLength(value: Int): this.type = set(NGramLength, value)
+  def setN(value: Int): this.type = set(n, value)
 
   /** @group getParam */
-  def getNGramLength: Int = $(NGramLength)
+  def getN: Int = $(n)
 
-  setDefault(NGramLength -> 2)
+  setDefault(n -> 2)
 
   override protected def createTransformFunc: Seq[String] => Seq[String] = {
-    val minLength = $(NGramLength)
+    val minLength = $(n)
     _.sliding(minLength).map(_.mkString(" ")).toSeq
   }
 
   override protected def validateInputType(inputType: DataType): Unit = {
-    require(
-      inputType.sameType(ArrayType(StringType)),
+    require(inputType.sameType(ArrayType(StringType)),
       s"Input type must be ArrayType(StringType) but got $inputType.")
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala
index a90d967fb48fa..034056905699f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NGramSuite.scala
@@ -30,7 +30,7 @@ class NGramSuite extends SparkFunSuite with MLlibTestSparkContext {
   import org.apache.spark.ml.feature.NGramSuite._
 
   test("default behavior yields bigram features") {
-    val tokenizer = new NGram()
+    val NGramTransformer = new NGram()
       .setInputCol("inputTokens")
       .setOutputCol("NGrams")
     val dataset = sqlContext.createDataFrame(Seq(
@@ -38,20 +38,45 @@ class NGramSuite extends SparkFunSuite with MLlibTestSparkContext {
         Array("Test", "for", "ngram", "."),
         Array("Test for", "for ngram", "ngram .")
     )))
-    testNGram(tokenizer, dataset)
+    testNGram(NGramTransformer, dataset)
   }
 
   test("NGramLength=4 yields length 4 n-grams") {
-    val tokenizer = new NGram()
+    val NGramTransformer = new NGram()
       .setInputCol("inputTokens")
       .setOutputCol("NGrams")
-      .setNGramLength(4)
+      .setN(4)
     val dataset = sqlContext.createDataFrame(Seq(
       NGramTestData(
         Array("a", "b", "c", "d", "e"),
         Array("a b c d", "b c d e")
       )))
-    testNGram(tokenizer, dataset)
+    testNGram(NGramTransformer, dataset)
+  }
+
+  test("empty input yields empty output") {
+    val NGramTransformer = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("NGrams")
+      .setN(4)
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array(),
+        Array()
+      )))
+    testNGram(NGramTransformer, dataset)
+  }
+  test("input array < n yields a single n-gram consisting of input array") {
+    val NGramTransformer = new NGram()
+      .setInputCol("inputTokens")
+      .setOutputCol("NGrams")
+      .setN(6)
+    val dataset = sqlContext.createDataFrame(Seq(
+      NGramTestData(
+        Array("a", "b", "c", "d", "e"),
+        Array("a b c d e")
+      )))
+    testNGram(NGramTransformer, dataset)
   }
 }
 
@@ -62,7 +87,7 @@ object NGramSuite extends SparkFunSuite {
       .select("NGrams", "wantedNGrams")
       .collect()
       .foreach { case Row(actualNGrams, wantedNGrams) =>
-      assert(actualNGrams === wantedNGrams)
-    }
+        assert(actualNGrams === wantedNGrams)
+      }
   }
 }