diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
index e564b68cb8..239e7d93ac 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs
@@ -4,7 +4,7 @@
 
 namespace Microsoft.ML.Samples.Dynamic
 {
-    public static class LdaTransform
+    public static class LatentDirichletAllocationTransform
     {
         public static void Example()
         {
@@ -30,7 +30,7 @@ public static void Example()
 
             // A pipeline for featurizing the "Review" column
             var pipeline = ml.Transforms.Text.ProduceWordBags(review).
-                Append(ml.Transforms.Text.LatentDirichletAllocation(review, ldaFeatures, numTopic:3));
+                Append(ml.Transforms.Text.LatentDirichletAllocation(review, ldaFeatures, numberOfTopics: 3));
 
             // The transformed data
             var transformer = pipeline.Fit(trainData);
diff --git a/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs b/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
index d094d444ba..1d1df76477 100644
--- a/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
+++ b/src/Microsoft.ML.StaticPipe/LdaStaticExtensions.cs
@@ -12,65 +12,65 @@ namespace Microsoft.ML.StaticPipe
     /// <summary>
     /// Information on the result of fitting a LDA transform.
     /// </summary>
-    public sealed class LdaFitResult
+    public sealed class LatentDirichletAllocationFitResult
     {
         /// <summary>
         /// For user defined delegates that accept instances of the containing type.
         /// </summary>
         /// <param name="result"></param>
-        public delegate void OnFit(LdaFitResult result);
+        public delegate void OnFit(LatentDirichletAllocationFitResult result);
 
         public LatentDirichletAllocationTransformer.LdaSummary LdaTopicSummary;
-        public LdaFitResult(LatentDirichletAllocationTransformer.LdaSummary ldaTopicSummary)
+        public LatentDirichletAllocationFitResult(LatentDirichletAllocationTransformer.LdaSummary ldaTopicSummary)
         {
             LdaTopicSummary = ldaTopicSummary;
         }
     }
 
-    public static class LdaStaticExtensions
+    public static class LatentDirichletAllocationStaticExtensions
     {
         private struct Config
         {
-            public readonly int NumTopic;
+            public readonly int NumberOfTopics;
             public readonly Single AlphaSum;
             public readonly Single Beta;
-            public readonly int MHStep;
-            public readonly int NumIter;
+            public readonly int SamplingStepCount;
+            public readonly int MaximumNumberOfIterations;
             public readonly int LikelihoodInterval;
-            public readonly int NumThread;
-            public readonly int NumMaxDocToken;
-            public readonly int NumSummaryTermPerTopic;
-            public readonly int NumBurninIter;
+            public readonly int NumberOfThreads;
+            public readonly int MaximumTokenCountPerDocument;
+            public readonly int NumberOfSummaryTermsPerTopic;
+            public readonly int NumberOfBurninIterations;
             public readonly bool ResetRandomGenerator;
 
             public readonly Action<LatentDirichletAllocationTransformer.LdaSummary> OnFit;
 
-            public Config(int numTopic, Single alphaSum, Single beta, int mhStep, int numIter, int likelihoodInterval,
-                int numThread, int numMaxDocToken, int numSummaryTermPerTopic, int numBurninIter, bool resetRandomGenerator,
+            public Config(int numberOfTopics, Single alphaSum, Single beta, int samplingStepCount, int maximumNumberOfIterations, int likelihoodInterval,
+                int numberOfThreads, int maximumTokenCountPerDocument, int numberOfSummaryTermsPerTopic, int numberOfBurninIterations, bool resetRandomGenerator,
                 Action<LatentDirichletAllocationTransformer.LdaSummary> onFit)
             {
-                NumTopic = numTopic;
+                NumberOfTopics = numberOfTopics;
                 AlphaSum = alphaSum;
                 Beta = beta;
-                MHStep = mhStep;
-                NumIter = numIter;
+                SamplingStepCount = samplingStepCount;
+                MaximumNumberOfIterations = maximumNumberOfIterations;
                 LikelihoodInterval = likelihoodInterval;
-                NumThread = numThread;
-                NumMaxDocToken = numMaxDocToken;
-                NumSummaryTermPerTopic = numSummaryTermPerTopic;
-                NumBurninIter = numBurninIter;
+                NumberOfThreads = numberOfThreads;
+                MaximumTokenCountPerDocument = maximumTokenCountPerDocument;
+                NumberOfSummaryTermsPerTopic = numberOfSummaryTermsPerTopic;
+                NumberOfBurninIterations = numberOfBurninIterations;
                 ResetRandomGenerator = resetRandomGenerator;
 
                 OnFit = onFit;
             }
         }
 
-        private static Action<LatentDirichletAllocationTransformer.LdaSummary> Wrap(LdaFitResult.OnFit onFit)
+        private static Action<LatentDirichletAllocationTransformer.LdaSummary> Wrap(LatentDirichletAllocationFitResult.OnFit onFit)
         {
             if (onFit == null)
                 return null;
 
-            return ldaTopicSummary => onFit(new LdaFitResult(ldaTopicSummary));
+            return ldaTopicSummary => onFit(new LatentDirichletAllocationFitResult(ldaTopicSummary));
         }
 
         private interface ILdaCol
@@ -108,16 +108,16 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 
                     infos[i] = new LatentDirichletAllocationEstimator.ColumnOptions(outputNames[toOutput[i]],
                         inputNames[tcol.Input],
-                        tcol.Config.NumTopic,
+                        tcol.Config.NumberOfTopics,
                         tcol.Config.AlphaSum,
                         tcol.Config.Beta,
-                        tcol.Config.MHStep,
-                        tcol.Config.NumIter,
+                        tcol.Config.SamplingStepCount,
+                        tcol.Config.MaximumNumberOfIterations,
                         tcol.Config.LikelihoodInterval,
-                        tcol.Config.NumThread,
-                        tcol.Config.NumMaxDocToken,
-                        tcol.Config.NumSummaryTermPerTopic,
-                        tcol.Config.NumBurninIter,
+                        tcol.Config.NumberOfThreads,
+                        tcol.Config.MaximumTokenCountPerDocument,
+                        tcol.Config.NumberOfSummaryTermsPerTopic,
+                        tcol.Config.NumberOfBurninIterations,
                         tcol.Config.ResetRandomGenerator);
 
                     if (tcol.Config.OnFit != null)
@@ -137,36 +137,36 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
 
         /// <include file='../Microsoft.ML.Transforms/Text/doc.xml' path='doc/members/member[@name="LightLDA"]/*' />
         /// <param name="input">A vector of floats representing the document.</param>
-        /// <param name="numTopic">The number of topics.</param>
+        /// <param name="numberOfTopics">The number of topics.</param>
         /// <param name="alphaSum">Dirichlet prior on document-topic vectors.</param>
         /// <param name="beta">Dirichlet prior on vocab-topic vectors.</param>
-        /// <param name="mhstep">Number of Metropolis Hasting step.</param>
-        /// <param name="numIterations">Number of iterations.</param>
+        /// <param name="samplingStepCount">Number of Metropolis Hasting step.</param>
+        /// <param name="maximumNumberOfIterations">Number of iterations.</param>
         /// <param name="likelihoodInterval">Compute log likelihood over local dataset on this iteration interval.</param>
-        /// <param name="numThreads">The number of training threads. Default value depends on number of logical processors.</param>
-        /// <param name="numMaxDocToken">The threshold of maximum count of tokens per doc.</param>
-        /// <param name="numSummaryTermPerTopic">The number of words to summarize the topic.</param>
-        /// <param name="numBurninIterations">The number of burn-in iterations.</param>
+        /// <param name="numberOfThreads">The number of training threads. Default value depends on number of logical processors.</param>
+        /// <param name="maximumTokenCountPerDocument">The threshold of maximum count of tokens per doc.</param>
+        /// <param name="numberOfSummaryTermsPerTopic">The number of words to summarize the topic.</param>
+        /// <param name="numberOfBurninIterations">The number of burn-in iterations.</param>
         /// <param name="resetRandomGenerator">Reset the random number generator for each document.</param>
         /// <param name="onFit">Called upon fitting with the learnt enumeration on the dataset.</param>
-        public static Vector<float> ToLdaTopicVector(this Vector<float> input,
-            int numTopic = LatentDirichletAllocationEstimator.Defaults.NumTopic,
+        public static Vector<float> LatentDirichletAllocation(this Vector<float> input,
+            int numberOfTopics = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
             Single alphaSum = LatentDirichletAllocationEstimator.Defaults.AlphaSum,
             Single beta = LatentDirichletAllocationEstimator.Defaults.Beta,
-            int mhstep = LatentDirichletAllocationEstimator.Defaults.Mhstep,
-            int numIterations = LatentDirichletAllocationEstimator.Defaults.NumIterations,
+            int samplingStepCount = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
+            int maximumNumberOfIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
             int likelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
-            int numThreads = LatentDirichletAllocationEstimator.Defaults.NumThreads,
-            int numMaxDocToken = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken,
-            int numSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic,
-            int numBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations,
+            int numberOfThreads = LatentDirichletAllocationEstimator.Defaults.NumberOfThreads,
+            int maximumTokenCountPerDocument = LatentDirichletAllocationEstimator.Defaults.MaximumTokenCountPerDocument,
+            int numberOfSummaryTermsPerTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfSummaryTermsPerTopic,
+            int numberOfBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumberOfBurninIterations,
             bool resetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator,
-            LdaFitResult.OnFit onFit = null)
+            LatentDirichletAllocationFitResult.OnFit onFit = null)
         {
             Contracts.CheckValue(input, nameof(input));
             return new ImplVector(input,
-                new Config(numTopic, alphaSum, beta, mhstep, numIterations, likelihoodInterval, numThreads, numMaxDocToken, numSummaryTermPerTopic,
-                numBurninIterations, resetRandomGenerator, Wrap(onFit)));
+                new Config(numberOfTopics, alphaSum, beta, samplingStepCount, maximumNumberOfIterations, likelihoodInterval, numberOfThreads, maximumTokenCountPerDocument, numberOfSummaryTermsPerTopic,
+                numberOfBurninIterations, resetRandomGenerator, Wrap(onFit)));
         }
     }
 }
\ No newline at end of file
diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
index 3c247ab1fd..aec1bfe763 100644
--- a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
+++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
@@ -58,7 +58,7 @@ internal sealed class Options : TransformInputBase
             [Argument(ArgumentType.AtMostOnce, HelpText = "The number of topics", SortOrder = 50)]
             [TGUI(SuggestedSweeps = "20,40,100,200")]
             [TlcModule.SweepableDiscreteParam("NumTopic", new object[] { 20, 40, 100, 200 })]
-            public int NumTopic = LatentDirichletAllocationEstimator.Defaults.NumTopic;
+            public int NumTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on document-topic vectors")]
             [TGUI(SuggestedSweeps = "1,10,100,200")]
@@ -73,30 +73,30 @@ internal sealed class Options : TransformInputBase
             [Argument(ArgumentType.Multiple, HelpText = "Number of Metropolis Hasting step")]
             [TGUI(SuggestedSweeps = "2,4,8,16")]
             [TlcModule.SweepableDiscreteParam("Mhstep", new object[] { 2, 4, 8, 16 })]
-            public int Mhstep = LatentDirichletAllocationEstimator.Defaults.Mhstep;
+            public int Mhstep = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter")]
             [TGUI(SuggestedSweeps = "100,200,300,400")]
             [TlcModule.SweepableDiscreteParam("NumIterations", new object[] { 100, 200, 300, 400 })]
-            public int NumIterations = LatentDirichletAllocationEstimator.Defaults.NumIterations;
+            public int NumIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "Compute log likelihood over local dataset on this iteration interval", ShortName = "llInterval")]
             public int LikelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval;
 
             // REVIEW: Should change the default when multi-threading support is optimized.
             [Argument(ArgumentType.AtMostOnce, HelpText = "The number of training threads. Default value depends on number of logical processors.", ShortName = "t", SortOrder = 50)]
-            public int NumThreads = LatentDirichletAllocationEstimator.Defaults.NumThreads;
+            public int NumThreads = LatentDirichletAllocationEstimator.Defaults.NumberOfThreads;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "The threshold of maximum count of tokens per doc", ShortName = "maxNumToken", SortOrder = 50)]
-            public int NumMaxDocToken = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken;
+            public int NumMaxDocToken = LatentDirichletAllocationEstimator.Defaults.MaximumTokenCountPerDocument;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "The number of words to summarize the topic", ShortName = "ns")]
-            public int NumSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic;
+            public int NumSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfSummaryTermsPerTopic;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "The number of burn-in iterations", ShortName = "burninIter")]
             [TGUI(SuggestedSweeps = "10,20,30,40")]
             [TlcModule.SweepableDiscreteParam("NumBurninIterations", new object[] { 10, 20, 30, 40 })]
-            public int NumBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations;
+            public int NumBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumberOfBurninIterations;
 
             [Argument(ArgumentType.AtMostOnce, HelpText = "Reset the random number generator for each document", ShortName = "reset")]
             public bool ResetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator;
@@ -219,17 +219,17 @@ internal LdaState(IExceptionContext ectx, LatentDirichletAllocationEstimator.Col
                 _numVocab = numVocab;
 
                 _ldaTrainer = new LdaSingleBox(
-                    InfoEx.NumTopic,
+                    InfoEx.NumberOfTopics,
                     numVocab, /* Need to set number of vocabulary here */
                     InfoEx.AlphaSum,
                     InfoEx.Beta,
-                    InfoEx.NumIter,
+                    InfoEx.NumberOfIterations,
                     InfoEx.LikelihoodInterval,
-                    InfoEx.NumThread,
-                    InfoEx.MHStep,
-                    InfoEx.NumSummaryTermPerTopic,
+                    InfoEx.NumberOfThreads,
+                    InfoEx.SamplingStepCount,
+                    InfoEx.NumberOfSummaryTermsPerTopic,
                     false,
-                    InfoEx.NumMaxDocToken);
+                    InfoEx.MaximumTokenCountPerDocument);
             }
 
             internal LdaState(IExceptionContext ectx, ModelLoadContext ctx)
@@ -257,19 +257,19 @@ internal LdaState(IExceptionContext ectx, ModelLoadContext ctx)
                 ectx.CheckDecode(aliasMemBlockSize > 0);
 
                 _ldaTrainer = new LdaSingleBox(
-                    InfoEx.NumTopic,
+                    InfoEx.NumberOfTopics,
                     _numVocab, /* Need to set number of vocabulary here */
                     InfoEx.AlphaSum,
                     InfoEx.Beta,
-                    InfoEx.NumIter,
+                    InfoEx.NumberOfIterations,
                     InfoEx.LikelihoodInterval,
-                    InfoEx.NumThread,
-                    InfoEx.MHStep,
-                    InfoEx.NumSummaryTermPerTopic,
+                    InfoEx.NumberOfThreads,
+                    InfoEx.SamplingStepCount,
+                    InfoEx.NumberOfSummaryTermsPerTopic,
                     false,
-                    InfoEx.NumMaxDocToken);
+                    InfoEx.MaximumTokenCountPerDocument);
 
-                _ldaTrainer.AllocateModelMemory(_numVocab, InfoEx.NumTopic, memBlockSize, aliasMemBlockSize);
+                _ldaTrainer.AllocateModelMemory(_numVocab, InfoEx.NumberOfTopics, memBlockSize, aliasMemBlockSize);
 
                 for (int i = 0; i < _numVocab; i++)
                 {
@@ -400,7 +400,7 @@ public int FeedTrain(IExceptionContext ectx, in VBuffer<Double> input)
                         // Ignore this row.
                         return 0;
                     }
-                    if (docSize >= InfoEx.NumMaxDocToken - termFreq)
+                    if (docSize >= InfoEx.MaximumTokenCountPerDocument - termFreq)
                         break;
 
                     // If legal then add the term.
@@ -448,7 +448,7 @@ public void Output(in VBuffer<Double> src, ref VBuffer<float> dst, int numBurnin
                     }
                 }
 
-                int len = InfoEx.NumTopic;
+                int len = InfoEx.NumberOfTopics;
                 var srcValues = src.GetValues();
                 if (srcValues.Length == 0)
                 {
@@ -476,7 +476,7 @@ public void Output(in VBuffer<Double> src, ref VBuffer<float> dst, int numBurnin
                         return;
                     }
 
-                    if (docSize >= InfoEx.NumMaxDocToken - termFreq)
+                    if (docSize >= InfoEx.MaximumTokenCountPerDocument - termFreq)
                         break;
 
                     docSize += termFreq;
@@ -557,7 +557,7 @@ protected override DataViewSchema.DetachedColumn[] GetOutputColumnsCore()
                 for (int i = 0; i < _parent.ColumnPairs.Length; i++)
                 {
                     var info = _parent._columns[i];
-                    result[i] = new DataViewSchema.DetachedColumn(_parent.ColumnPairs[i].outputColumnName, new VectorType(NumberDataViewType.Single, info.NumTopic), null);
+                    result[i] = new DataViewSchema.DetachedColumn(_parent.ColumnPairs[i].outputColumnName, new VectorType(NumberDataViewType.Single, info.NumberOfTopics), null);
                 }
                 return result;
             }
@@ -576,7 +576,7 @@ private ValueGetter<VBuffer<float>> GetTopic(DataViewRow input, int iinfo)
                 var getSrc = RowCursorUtils.GetVecGetterAs<Double>(NumberDataViewType.Double, input, _srcCols[iinfo]);
                 var src = default(VBuffer<Double>);
                 var lda = _parent._ldas[iinfo];
-                int numBurninIter = lda.InfoEx.NumBurninIter;
+                int numBurninIter = lda.InfoEx.NumberOfBurninIterations;
                 bool reset = lda.InfoEx.ResetRandomGenerator;
                 return
                     (ref VBuffer<float> dst) =>
@@ -831,7 +831,7 @@ private static List<VBuffer<ReadOnlyMemory<char>>> Train(IHostEnvironment env, I
                                 break;
                             }
 
-                            if (docSize >= columns[i].NumMaxDocToken - termFreq)
+                            if (docSize >= columns[i].MaximumTokenCountPerDocument - termFreq)
                                 break; //control the document length
 
                             //if legal then add the term
@@ -920,16 +920,16 @@ public sealed class LatentDirichletAllocationEstimator : IEstimator<LatentDirich
         [BestFriend]
         internal static class Defaults
         {
-            public const int NumTopic = 100;
+            public const int NumberOfTopics = 100;
             public const float AlphaSum = 100;
             public const float Beta = 0.01f;
-            public const int Mhstep = 4;
-            public const int NumIterations = 200;
+            public const int SamplingStepCount = 4;
+            public const int MaximumNumberOfIterations = 200;
             public const int LikelihoodInterval = 5;
-            public const int NumThreads = 0;
-            public const int NumMaxDocToken = 512;
-            public const int NumSummaryTermPerTopic = 10;
-            public const int NumBurninIterations = 10;
+            public const int NumberOfThreads = 0;
+            public const int MaximumTokenCountPerDocument = 512;
+            public const int NumberOfSummaryTermsPerTopic = 10;
+            public const int NumberOfBurninIterations = 10;
             public const bool ResetRandomGenerator = false;
         }
 
@@ -940,33 +940,33 @@ internal static class Defaults
         /// <param name="env">The environment.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="numTopic">The number of topics.</param>
+        /// <param name="numberOfTopics">The number of topics.</param>
         /// <param name="alphaSum">Dirichlet prior on document-topic vectors.</param>
         /// <param name="beta">Dirichlet prior on vocab-topic vectors.</param>
-        /// <param name="mhstep">Number of Metropolis Hasting step.</param>
-        /// <param name="numIterations">Number of iterations.</param>
+        /// <param name="samplingStepCount">Number of Metropolis Hasting step.</param>
+        /// <param name="maximumNumberOfIterations">Number of iterations.</param>
+        /// <param name="numberOfThreads">The number of training threads. Default value depends on number of logical processors.</param>
+        /// <param name="maximumTokenCountPerDocument">The threshold of maximum count of tokens per doc.</param>
+        /// <param name="numberOfSummaryTermsPerTopic">The number of words to summarize the topic.</param>
         /// <param name="likelihoodInterval">Compute log likelihood over local dataset on this iteration interval.</param>
-        /// <param name="numThreads">The number of training threads. Default value depends on number of logical processors.</param>
-        /// <param name="numMaxDocToken">The threshold of maximum count of tokens per doc.</param>
-        /// <param name="numSummaryTermPerTopic">The number of words to summarize the topic.</param>
-        /// <param name="numBurninIterations">The number of burn-in iterations.</param>
+        /// <param name="numberOfBurninIterations">The number of burn-in iterations.</param>
         /// <param name="resetRandomGenerator">Reset the random number generator for each document.</param>
         internal LatentDirichletAllocationEstimator(IHostEnvironment env,
             string outputColumnName, string inputColumnName = null,
-            int numTopic = Defaults.NumTopic,
+            int numberOfTopics = Defaults.NumberOfTopics,
             float alphaSum = Defaults.AlphaSum,
             float beta = Defaults.Beta,
-            int mhstep = Defaults.Mhstep,
-            int numIterations = Defaults.NumIterations,
+            int samplingStepCount = Defaults.SamplingStepCount,
+            int maximumNumberOfIterations = Defaults.MaximumNumberOfIterations,
+            int numberOfThreads = Defaults.NumberOfThreads,
+            int maximumTokenCountPerDocument = Defaults.MaximumTokenCountPerDocument,
+            int numberOfSummaryTermsPerTopic = Defaults.NumberOfSummaryTermsPerTopic,
             int likelihoodInterval = Defaults.LikelihoodInterval,
-            int numThreads = Defaults.NumThreads,
-            int numMaxDocToken = Defaults.NumMaxDocToken,
-            int numSummaryTermPerTopic = Defaults.NumSummaryTermPerTopic,
-            int numBurninIterations = Defaults.NumBurninIterations,
+            int numberOfBurninIterations = Defaults.NumberOfBurninIterations,
             bool resetRandomGenerator = Defaults.ResetRandomGenerator)
             : this(env, new[] { new ColumnOptions(outputColumnName, inputColumnName ?? outputColumnName,
-                numTopic, alphaSum, beta, mhstep, numIterations, likelihoodInterval, numThreads, numMaxDocToken,
-                numSummaryTermPerTopic, numBurninIterations, resetRandomGenerator) })
+                numberOfTopics, alphaSum, beta, samplingStepCount, maximumNumberOfIterations, likelihoodInterval, numberOfThreads, maximumTokenCountPerDocument,
+                numberOfSummaryTermsPerTopic, numberOfBurninIterations, resetRandomGenerator) })
         { }
 
         /// <include file='doc.xml' path='doc/members/member[@name="LightLDA"]/*' />
@@ -995,7 +995,7 @@ public sealed class ColumnOptions
             /// <summary>
             /// The number of topics.
             /// </summary>
-            public readonly int NumTopic;
+            public readonly int NumberOfTopics;
             /// <summary>
             /// Dirichlet prior on document-topic vectors.
             /// </summary>
@@ -1007,11 +1007,11 @@ public sealed class ColumnOptions
             /// <summary>
             /// Number of Metropolis Hasting step.
             /// </summary>
-            public readonly int MHStep;
+            public readonly int SamplingStepCount;
             /// <summary>
             /// Number of iterations.
             /// </summary>
-            public readonly int NumIter;
+            public readonly int NumberOfIterations;
             /// <summary>
             /// Compute log likelihood over local dataset on this iteration interval.
             /// </summary>
@@ -1019,19 +1019,19 @@ public sealed class ColumnOptions
             /// <summary>
             /// The number of training threads.
             /// </summary>
-            public readonly int NumThread;
+            public readonly int NumberOfThreads;
             /// <summary>
             /// The threshold of maximum count of tokens per doc.
             /// </summary>
-            public readonly int NumMaxDocToken;
+            public readonly int MaximumTokenCountPerDocument;
             /// <summary>
             /// The number of words to summarize the topic.
             /// </summary>
-            public readonly int NumSummaryTermPerTopic;
+            public readonly int NumberOfSummaryTermsPerTopic;
             /// <summary>
             /// The number of burn-in iterations.
             /// </summary>
-            public readonly int NumBurninIter;
+            public readonly int NumberOfBurninIterations;
             /// <summary>
             /// Reset the random number generator for each document.
             /// </summary>
@@ -1042,54 +1042,54 @@ public sealed class ColumnOptions
             /// </summary>
             /// <param name="name">The column containing the output scores over a set of topics, represented as a vector of floats. </param>
             /// <param name="inputColumnName">The column representing the document as a vector of floats.A null value for the column means <paramref name="inputColumnName"/> is replaced. </param>
-            /// <param name="numTopic">The number of topics.</param>
+            /// <param name="numberOfTopics">The number of topics.</param>
             /// <param name="alphaSum">Dirichlet prior on document-topic vectors.</param>
             /// <param name="beta">Dirichlet prior on vocab-topic vectors.</param>
-            /// <param name="mhStep">Number of Metropolis Hasting step.</param>
-            /// <param name="numIter">Number of iterations.</param>
+            /// <param name="samplingStepCount">Number of Metropolis Hasting step.</param>
+            /// <param name="maximumNumberOfIterations">Number of iterations.</param>
             /// <param name="likelihoodInterval">Compute log likelihood over local dataset on this iteration interval.</param>
-            /// <param name="numThread">The number of training threads. Default value depends on number of logical processors.</param>
-            /// <param name="numMaxDocToken">The threshold of maximum count of tokens per doc.</param>
-            /// <param name="numSummaryTermPerTopic">The number of words to summarize the topic.</param>
-            /// <param name="numBurninIter">The number of burn-in iterations.</param>
+            /// <param name="numberOfThreads">The number of training threads. Default value depends on number of logical processors.</param>
+            /// <param name="maximumTokenCountPerDocument">The threshold of maximum count of tokens per doc.</param>
+            /// <param name="numberOfSummaryTermsPerTopic">The number of words to summarize the topic.</param>
+            /// <param name="numberOfBurninIterations">The number of burn-in iterations.</param>
             /// <param name="resetRandomGenerator">Reset the random number generator for each document.</param>
             public ColumnOptions(string name,
                 string inputColumnName = null,
-                int numTopic = LatentDirichletAllocationEstimator.Defaults.NumTopic,
+                int numberOfTopics = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
                 float alphaSum = LatentDirichletAllocationEstimator.Defaults.AlphaSum,
                 float beta = LatentDirichletAllocationEstimator.Defaults.Beta,
-                int mhStep = LatentDirichletAllocationEstimator.Defaults.Mhstep,
-                int numIter = LatentDirichletAllocationEstimator.Defaults.NumIterations,
+                int samplingStepCount = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
+                int maximumNumberOfIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
                 int likelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
-                int numThread = LatentDirichletAllocationEstimator.Defaults.NumThreads,
-                int numMaxDocToken = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken,
-                int numSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic,
-                int numBurninIter = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations,
+                int numberOfThreads = LatentDirichletAllocationEstimator.Defaults.NumberOfThreads,
+                int maximumTokenCountPerDocument = LatentDirichletAllocationEstimator.Defaults.MaximumTokenCountPerDocument,
+                int numberOfSummaryTermsPerTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfSummaryTermsPerTopic,
+                int numberOfBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumberOfBurninIterations,
                 bool resetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator)
             {
                 Contracts.CheckValue(name, nameof(name));
                 Contracts.CheckValueOrNull(inputColumnName);
-                Contracts.CheckParam(numTopic > 0, nameof(numTopic), "Must be positive.");
-                Contracts.CheckParam(mhStep > 0, nameof(mhStep), "Must be positive.");
-                Contracts.CheckParam(numIter > 0, nameof(numIter), "Must be positive.");
+                Contracts.CheckParam(numberOfTopics > 0, nameof(numberOfTopics), "Must be positive.");
+                Contracts.CheckParam(samplingStepCount > 0, nameof(samplingStepCount), "Must be positive.");
+                Contracts.CheckParam(maximumNumberOfIterations > 0, nameof(maximumNumberOfIterations), "Must be positive.");
                 Contracts.CheckParam(likelihoodInterval > 0, nameof(likelihoodInterval), "Must be positive.");
-                Contracts.CheckParam(numThread >= 0, nameof(numThread), "Must be positive or zero.");
-                Contracts.CheckParam(numMaxDocToken > 0, nameof(numMaxDocToken), "Must be positive.");
-                Contracts.CheckParam(numSummaryTermPerTopic > 0, nameof(numSummaryTermPerTopic), "Must be positive");
-                Contracts.CheckParam(numBurninIter >= 0, nameof(numBurninIter), "Must be non-negative.");
+                Contracts.CheckParam(numberOfThreads >= 0, nameof(numberOfThreads), "Must be positive or zero.");
+                Contracts.CheckParam(maximumTokenCountPerDocument > 0, nameof(maximumTokenCountPerDocument), "Must be positive.");
+                Contracts.CheckParam(numberOfSummaryTermsPerTopic > 0, nameof(numberOfSummaryTermsPerTopic), "Must be positive");
+                Contracts.CheckParam(numberOfBurninIterations >= 0, nameof(numberOfBurninIterations), "Must be non-negative.");
 
                 Name = name;
                 InputColumnName = inputColumnName ?? name;
-                NumTopic = numTopic;
+                NumberOfTopics = numberOfTopics;
                 AlphaSum = alphaSum;
                 Beta = beta;
-                MHStep = mhStep;
-                NumIter = numIter;
+                SamplingStepCount = samplingStepCount;
+                NumberOfIterations = maximumNumberOfIterations;
                 LikelihoodInterval = likelihoodInterval;
-                NumThread = numThread;
-                NumMaxDocToken = numMaxDocToken;
-                NumSummaryTermPerTopic = numSummaryTermPerTopic;
-                NumBurninIter = numBurninIter;
+                NumberOfThreads = numberOfThreads;
+                MaximumTokenCountPerDocument = maximumTokenCountPerDocument;
+                NumberOfSummaryTermsPerTopic = numberOfSummaryTermsPerTopic;
+                NumberOfBurninIterations = numberOfBurninIterations;
                 ResetRandomGenerator = resetRandomGenerator;
             }
 
@@ -1128,33 +1128,33 @@ internal ColumnOptions(IExceptionContext ectx, ModelLoadContext ctx)
                 // int NumBurninIter;
                 // byte ResetRandomGenerator;
 
-                NumTopic = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumTopic > 0);
+                NumberOfTopics = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(NumberOfTopics > 0);
 
                 AlphaSum = ctx.Reader.ReadSingle();
 
                 Beta = ctx.Reader.ReadSingle();
 
-                MHStep = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(MHStep > 0);
+                SamplingStepCount = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(SamplingStepCount > 0);
 
-                NumIter = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumIter > 0);
+                NumberOfIterations = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(NumberOfIterations > 0);
 
                 LikelihoodInterval = ctx.Reader.ReadInt32();
                 ectx.CheckDecode(LikelihoodInterval > 0);
 
-                NumThread = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumThread >= 0);
+                NumberOfThreads = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(NumberOfThreads >= 0);
 
-                NumMaxDocToken = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumMaxDocToken > 0);
+                MaximumTokenCountPerDocument = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(MaximumTokenCountPerDocument > 0);
 
-                NumSummaryTermPerTopic = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumSummaryTermPerTopic > 0);
+                NumberOfSummaryTermsPerTopic = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(NumberOfSummaryTermsPerTopic > 0);
 
-                NumBurninIter = ctx.Reader.ReadInt32();
-                ectx.CheckDecode(NumBurninIter >= 0);
+                NumberOfBurninIterations = ctx.Reader.ReadInt32();
+                ectx.CheckDecode(NumberOfBurninIterations >= 0);
 
                 ResetRandomGenerator = ctx.Reader.ReadBoolByte();
             }
@@ -1176,16 +1176,16 @@ internal void Save(ModelSaveContext ctx)
                 // int NumBurninIter;
                 // byte ResetRandomGenerator;
 
-                ctx.Writer.Write(NumTopic);
+                ctx.Writer.Write(NumberOfTopics);
                 ctx.Writer.Write(AlphaSum);
                 ctx.Writer.Write(Beta);
-                ctx.Writer.Write(MHStep);
-                ctx.Writer.Write(NumIter);
+                ctx.Writer.Write(SamplingStepCount);
+                ctx.Writer.Write(NumberOfIterations);
                 ctx.Writer.Write(LikelihoodInterval);
-                ctx.Writer.Write(NumThread);
-                ctx.Writer.Write(NumMaxDocToken);
-                ctx.Writer.Write(NumSummaryTermPerTopic);
-                ctx.Writer.Write(NumBurninIter);
+                ctx.Writer.Write(NumberOfThreads);
+                ctx.Writer.Write(MaximumTokenCountPerDocument);
+                ctx.Writer.Write(NumberOfSummaryTermsPerTopic);
+                ctx.Writer.Write(NumberOfBurninIterations);
                 ctx.Writer.WriteBoolByte(ResetRandomGenerator);
             }
         }
diff --git a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
index 92647a4ab0..230ed970d4 100644
--- a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
+++ b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
@@ -579,17 +579,10 @@ public static NgramHashingEstimator ProduceHashedNgrams(this TransformsCatalog.T
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="numTopic">The number of topics.</param>
-        /// <param name="alphaSum">Dirichlet prior on document-topic vectors.</param>
-        /// <param name="beta">Dirichlet prior on vocab-topic vectors.</param>
-        /// <param name="mhstep">Number of Metropolis Hasting step.</param>
-        /// <param name="numIterations">Number of iterations.</param>
-        /// <param name="likelihoodInterval">Compute log likelihood over local dataset on this iteration interval.</param>
-        /// <param name="numThreads">The number of training threads. Default value depends on number of logical processors.</param>
-        /// <param name="numMaxDocToken">The threshold of maximum count of tokens per doc.</param>
-        /// <param name="numSummaryTermPerTopic">The number of words to summarize the topic.</param>
-        /// <param name="numBurninIterations">The number of burn-in iterations.</param>
-        /// <param name="resetRandomGenerator">Reset the random number generator for each document.</param>
+        /// <param name="numberOfTopics">The number of topics.</param>
+        /// <param name="maximumNumberOfIterations">Number of iterations.</param>
+        /// <param name="maximumTokenCountPerDocument">The threshold of maximum count of tokens per doc.</param>
+        /// <param name="numberOfSummaryTermsPerTopic">The number of words to summarize the topic.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
@@ -600,20 +593,22 @@ public static NgramHashingEstimator ProduceHashedNgrams(this TransformsCatalog.T
         public static LatentDirichletAllocationEstimator LatentDirichletAllocation(this TransformsCatalog.TextTransforms catalog,
             string outputColumnName,
             string inputColumnName = null,
-            int numTopic = LatentDirichletAllocationEstimator.Defaults.NumTopic,
-            float alphaSum = LatentDirichletAllocationEstimator.Defaults.AlphaSum,
-            float beta = LatentDirichletAllocationEstimator.Defaults.Beta,
-            int mhstep = LatentDirichletAllocationEstimator.Defaults.Mhstep,
-            int numIterations = LatentDirichletAllocationEstimator.Defaults.NumIterations,
-            int likelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
-            int numThreads = LatentDirichletAllocationEstimator.Defaults.NumThreads,
-            int numMaxDocToken = LatentDirichletAllocationEstimator.Defaults.NumMaxDocToken,
-            int numSummaryTermPerTopic = LatentDirichletAllocationEstimator.Defaults.NumSummaryTermPerTopic,
-            int numBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumBurninIterations,
-            bool resetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator)
+            int numberOfTopics = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
+            int maximumNumberOfIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
+            int maximumTokenCountPerDocument = LatentDirichletAllocationEstimator.Defaults.MaximumTokenCountPerDocument,
+            int numberOfSummaryTermsPerTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfSummaryTermsPerTopic)
             => new LatentDirichletAllocationEstimator(CatalogUtils.GetEnvironment(catalog),
-                outputColumnName, inputColumnName, numTopic, alphaSum, beta, mhstep, numIterations, likelihoodInterval, numThreads,
-                numMaxDocToken, numSummaryTermPerTopic, numBurninIterations, resetRandomGenerator);
+                outputColumnName, inputColumnName, numberOfTopics,
+                LatentDirichletAllocationEstimator.Defaults.AlphaSum,
+                LatentDirichletAllocationEstimator.Defaults.Beta,
+                LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
+                maximumNumberOfIterations,
+                LatentDirichletAllocationEstimator.Defaults.NumberOfThreads,
+                maximumTokenCountPerDocument,
+                numberOfSummaryTermsPerTopic,
+                LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
+                LatentDirichletAllocationEstimator.Defaults.NumberOfBurninIterations,
+                LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator);
 
         /// <summary>
         /// Uses <a href="https://arxiv.org/abs/1412.1576">LightLDA</a> to transform a document (represented as a vector of floats)
diff --git a/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs b/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs
index 08f5431a55..933eab22e3 100644
--- a/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs
+++ b/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs
@@ -183,7 +183,7 @@ public void InspectLdaModelParameters()
 
             // Define the pipeline.
             var pipeline = mlContext.Transforms.Text.ProduceWordBags("SentimentBag", "SentimentText")
-                .Append(mlContext.Transforms.Text.LatentDirichletAllocation("Features", "SentimentBag", numTopic: numTopics, numIterations: 10));
+                .Append(mlContext.Transforms.Text.LatentDirichletAllocation("Features", "SentimentBag", numberOfTopics: numTopics, maximumNumberOfIterations: 10));
 
             // Fit the pipeline.
             var model = pipeline.Fit(data);
diff --git a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
index 813ec4c352..9796e8021d 100644
--- a/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
+++ b/test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs
@@ -675,7 +675,7 @@ public void LdaTopicModel()
             var est = data.MakeNewEstimator()
                 .Append(r => (
                     r.label,
-                    topics: r.text.ToBagofWords().ToLdaTopicVector(numTopic: 3, numSummaryTermPerTopic:5, alphaSum: 10, onFit: m => ldaSummary = m.LdaTopicSummary)));
+                    topics: r.text.ToBagofWords().LatentDirichletAllocation(numberOfTopics: 3, numberOfSummaryTermsPerTopic:5, alphaSum: 10, onFit: m => ldaSummary = m.LdaTopicSummary)));
 
             var transformer = est.Fit(data);
             var tdata = transformer.Transform(data);
diff --git a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs
index 56ae2fbc0e..dc2e5a1584 100644
--- a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs
+++ b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs
@@ -11,6 +11,7 @@
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Transforms;
+using Microsoft.ML.Transforms.Text;
 using Xunit;
 
 namespace Microsoft.ML.RunTests
@@ -1318,7 +1319,9 @@ public void TestLDATransform()
             builder.AddColumn("F1V", NumberDataViewType.Single, data);
             var srcView = builder.GetDataView();
 
-            var est = ML.Transforms.Text.LatentDirichletAllocation("F1V", numTopic: 3, numSummaryTermPerTopic: 3, alphaSum: 3, numThreads: 1, resetRandomGenerator: true);
+            var opt = new LatentDirichletAllocationEstimator.ColumnOptions(name: "F1V", numberOfTopics: 3,
+                numberOfSummaryTermsPerTopic: 3, alphaSum: 3, numberOfThreads: 1, resetRandomGenerator: true);
+            var est = ML.Transforms.Text.LatentDirichletAllocation(opt);
             var ldaTransformer = est.Fit(srcView);
             var transformedData = ldaTransformer.Transform(srcView);
 
diff --git a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
index 13c97bbcfb..ecaf5cd36e 100644
--- a/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
+++ b/test/Microsoft.ML.Tests/Transformers/TextFeaturizerTests.cs
@@ -288,7 +288,7 @@ public void LdaWorkout()
                 .Load(sentimentDataPath);
 
             var est = new WordBagEstimator(env, "bag_of_words", "text").
-                Append(new LatentDirichletAllocationEstimator(env, "topics", "bag_of_words", 10, numIterations: 10,
+                Append(new LatentDirichletAllocationEstimator(env, "topics", "bag_of_words", 10, maximumNumberOfIterations: 10,
                     resetRandomGenerator: true));
 
             // The following call fails because of the following issue