diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index c3623a83fb961..ebfd1299431be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -431,19 +431,28 @@ class Word2Vec extends Serializable with Logging {
 class Word2VecModel private[mllib] (
     model: Map[String, Array[Float]]) extends Serializable with Saveable {
 
-  // Maintain a ordered list of words based on the index in the initial model.
+  // wordList: Ordered list of words obtained from model.
+  // wordIndex: Maps each word to an index, which can retrieve the corresponding
+  //            vector from wordVectors (see below)
+  // vectorSize: Dimension of each vector.
+  // numWords: Number of words.
   private val wordList: Array[String] = model.keys.toArray
   private val wordIndex: Map[String, Int] = wordList.zip(0 until model.size).toMap
-  private val numDim = model.head._2.size
+  private val vectorSize = model.head._2.size
   private val numWords = wordIndex.size
 
+  // wordVectors: Array of length numWords * vectorSize, vector corresponding to the word
+  //              mapped with index i can be retrieved by the slice
+  //              (ind * vectorSize, ind * vectorSize + vectorSize)
+  // wordVecNorms: Array of length numWords, each value being the Euclidean norm
+  //               of the wordVector.
   private val (wordVectors: Array[Float], wordVecNorms: Array[Double]) = {
-    val wordVectors = model.toSeq.flatMap { case (w, v) => v }.toArray
+    val wordVectors = wordList.flatMap(word => model.get(word).get).toArray
     val wordVecNorms = new Array[Double](numWords)
     var i = 0
     while (i < numWords) {
       val vec = model.get(wordList(i)).get
-      wordVecNorms(i) = blas.snrm2(numDim, vec, 1)
+      wordVecNorms(i) = blas.snrm2(vectorSize, vec, 1)
       i += 1
     }
     (wordVectors, wordVecNorms)
@@ -501,10 +510,10 @@ class Word2VecModel private[mllib] (
     val fVector = vector.toArray.map(_.toFloat)
     val cosineVec = Array.fill[Float](numWords)(0)
     val alpha: Float = 1
-    val beta: Float = 1
+    val beta: Float = 0
 
     blas.sgemv(
-      "T", numDim, numWords, alpha, wordVectors, numDim, fVector, 1, beta, cosineVec, 1)
+      "T", vectorSize, numWords, alpha, wordVectors, vectorSize, fVector, 1, beta, cosineVec, 1)
 
     // Need not divide with the norm of the given vector since it is constant.
     val updatedCosines = new Array[Double](numWords)
@@ -526,7 +535,7 @@ class Word2VecModel private[mllib] (
    */
   def getVectors: Map[String, Array[Float]] = {
     wordIndex.map { case (word, ind) =>
-      (word, wordVectors.slice(numDim * ind, numDim * ind + numDim))
+      (word, wordVectors.slice(vectorSize * ind, vectorSize * ind + vectorSize))
     }
   }
 }