diff --git a/src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs b/src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs
index d54a165890..5082aa8da7 100644
--- a/src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs
+++ b/src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs
@@ -28,7 +28,7 @@ public static class ConversionsExtensionsCatalog
/// are vectors or scalars.</param>
/// <param name="inputColumnName">Name of the column whose data will be hashed.
/// If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.
- /// This estimator operates over text, numeric, boolean, key or <see cref="DataViewRowId"/> data types. </param>
+ /// This estimator operates over vectors or scalars of text, numeric, boolean, key or <see cref="DataViewRowId"/> data types. </param>
/// <param name="numberOfBits">Number of bits to hash into. Must be between 1 and 31, inclusive.</param>
/// <param name="maximumNumberOfInverts">During hashing we construct mappings between original values and the produced hash values.
/// Text representation of original values are stored in the slot names of the annotations for the new column.Hashing, as such, can map many initial values to one.
diff --git a/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs b/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs
index 2fdc8e6eb3..70b549ee76 100644
--- a/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs
+++ b/src/Microsoft.ML.Data/Transforms/ValueToKeyMappingEstimator.cs
@@ -21,7 +21,7 @@ namespace Microsoft.ML.Transforms
/// | | |
/// | -- | -- |
/// | Does this estimator need to look at the data to train its parameters? | Yes |
- /// | Input column data type | Vector or primitive numeric, boolean, [text](xref:Microsoft.ML.Data.TextDataViewType), [System.DateTime](xref:System.DateTime) and [key](xref:Microsoft.ML.Data.KeyDataViewType) data types.|
+ /// | Input column data type | Scalar numeric, boolean, [text](xref:Microsoft.ML.Data.TextDataViewType), [System.DateTime](xref:System.DateTime) or [key](xref:Microsoft.ML.Data.KeyDataViewType) data types.|
/// | Output column data type | [key](xref:Microsoft.ML.Data.KeyDataViewType)|
///
/// The ValueToKeyMappingEstimator builds up term vocabularies(dictionaries) mapping the input values to the keys on the dictionary.
diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
index 218e77aad0..9b4489379b 100644
--- a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
+++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs
@@ -46,7 +46,9 @@ namespace Microsoft.ML.Transforms.Text
//
// See <a href="https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs"/>
// for an example on how to use LatentDirichletAllocationTransformer.
- /// <include file='doc.xml' path='doc/members/member[@name="LightLDA"]/*' />
+ /// <summary>
+ /// <see cref="ITransformer"/> resulting from fitting a <see cref="LatentDirichletAllocationEstimator"/>.
+ /// </summary>
public sealed class LatentDirichletAllocationTransformer : OneToOneTransformerBase
{
internal sealed class Options : TransformInputBase
@@ -936,7 +938,56 @@ private protected override IRowMapper MakeRowMapper(DataViewSchema schema)
=> new Mapper(this, schema);
}
- /// <include file='doc.xml' path='doc/members/member[@name="LightLDA"]/*' />
+ /// <summary>
+ /// The LDA transform implements <a href="https://arxiv.org/abs/1412.1576">LightLDA</a>, a state-of-the-art implementation of Latent Dirichlet Allocation.
+ /// </summary>
+ /// <remarks>
+ /// <format type="text/markdown">< algorithm that infers semantic structure from text data,
+ /// and ultimately helps answer the question on "what is this document about?".
+ /// It can be used to featurize any text fields as low-dimensional topical vectors.
+ /// LightLDA is an extremely efficient implementation of LDA that incorporates a number of
+ /// optimization techniques.
+ /// With the LDA transform, ML.NET users can train a topic model to produce 1 million topics with 1 million words vocabulary
+ /// on a 1-billion-token document set one a single machine in a few hours(typically, LDA at this scale takes days and requires large clusters).
+ /// The most significant innovation is a super-efficient $O(1)$. [Metropolis-Hastings sampling algorithm](https://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm),
+ /// whose running cost is agnostic of model size, allowing it to converges nearly an order of magnitude faster than other [Gibbs samplers](https://en.wikipedia.org/wiki/Gibbs_sampling).
+ ///
+ /// In an ML.NET pipeline, this estimator requires the output of some preprocessing, as its input.
+ /// A typical pipeline operating on text would require text normalization, tokenization and producing n-grams to supply to the LDA estimator.
+ /// See the example usage in the See Also section for usage suggestions.
+ ///
+ /// If we have the following three examples of text, as data points, and use the LDA transform with the number of topics set to 3,
+ /// we would get the results displayed in the table below. Example documents:
+ /// * I like to eat bananas.
+ /// * I eat bananas everyday.
+ /// * First celebrated in 1970, Earth Day now includes events in more than 193 countries,
+ /// which are now coordinated globally by the Earth Day Network.
+ ///
+ /// Notice the similarity in values of the first and second row, compared to the third,
+ /// and see how those values are indicative of similarities between those two (small) bodies of text.
+ ///
+ /// | Topic1 | Topic2 | Topic 3 |
+ /// | ------- | ------- | ------- |
+ /// | 0.5714 | 0.0000 | 0.4286 |
+ /// | 0.5714 | 0.0000 | 0.4286 |
+ /// | 0.2400 | 0.3200 | 0.4400 |
+ ///
+ /// For more technical details you can consult the following papers.
+ /// * [LightLDA: Big Topic Models on Modest Computer Clusters](https://arxiv.org/abs/1412.1576)
+ /// * [LightLDA](https://github.com/Microsoft/LightLDA)
+ ///
+ /// ]]></format>
+ /// </remarks>
+ /// <seealso cref="TextCatalog.LatentDirichletAllocation(TransformsCatalog.TextTransforms, string, string, int, float, float, int, int, int, int, int, int, int, bool)"/>
public sealed class LatentDirichletAllocationEstimator : IEstimator<LatentDirichletAllocationTransformer>
{
[BestFriend]
diff --git a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
index f7410a419b..c6e8d432a6 100644
--- a/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
+++ b/src/Microsoft.ML.Transforms/Text/TextCatalog.cs
@@ -556,12 +556,15 @@ internal static NgramHashingEstimator ProduceHashedNgrams(this TransformsCatalog
=> new NgramHashingEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), columns);
/// <summary>
- /// Uses <a href="https://arxiv.org/abs/1412.1576">LightLDA</a> to transform a document (represented as a vector of floats)
- /// into a vector of floats over a set of topics.
+ /// Create a <see cref="LatentDirichletAllocationEstimator"/>, which uses <a href="https://arxiv.org/abs/1412.1576">LightLDA</a> to transform text (represented as a vector of floats)
+ /// into a vector of <see cref="System.Single"/> indicating the similarity of the text with each topic identified.
/// </summary>
/// <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="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.
+ /// This estimator outputs a vector of <see cref="System.Single"/>.</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.
+ /// This estimator operates over a vector of <see cref="System.Single"/>.
+ /// </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>
diff --git a/src/Microsoft.ML.Transforms/Text/doc.xml b/src/Microsoft.ML.Transforms/Text/doc.xml
index bc590b9332..c4da4d5f94 100644
--- a/src/Microsoft.ML.Transforms/Text/doc.xml
+++ b/src/Microsoft.ML.Transforms/Text/doc.xml
@@ -150,40 +150,6 @@
</example>
</member>
- <member name="LightLDA">
- <summary>
- The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation.
- </summary>
- <remarks>
- Latent Dirichlet Allocation is a well-known topic modeling algorithm that infers topical structure from text data,
- and can be used to featurize any text fields as low-dimensional topical vectors.
- <para>LightLDA is an extremely efficient implementation of LDA developed in MSR-Asia that incorporates a number of
- optimization techniques. See <a href="https://arxiv.org/abs/1412.1576">LightLDA: Big Topic Models on Modest Compute Clusters</a>.
- </para>
- <para>
- With the LDA transform, ML.NET users can train a topic model to produce 1 million topics with 1 million vocabulary
- on a 1-billion-token document set one a single machine in a few hours (typically, LDA at this scale takes days and requires large clusters).
- The most significant innovation is a super-efficient O(1) <a href="https://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm">Metropolis-Hastings sampling algorithm</a>,
- whose running cost is (surprisingly) agnostic of model size,
- allowing it to converges nearly an order of magnitude faster than other <a href="https://en.wikipedia.org/wiki/Gibbs_sampling">Gibbs samplers.</a>
- </para>
- <para>
- For more details please see original LightLDA paper, and its open source implementation.
- <list type="bullet">
- <item><description><a href="https://arxiv.org/abs/1412.1576"> LightLDA: Big Topic Models on Modest Computer Clusters</a></description></item>
- <item><description><a href=" https://github.com/Microsoft/LightLDA">LightLDA </a></description></item>
- </list>
- </para>
- </remarks>
- </member>
- <example name="LightLDA">
- <example>
- <code language="csharp">
- pipeline.Add(new LightLda(("InTextCol" , "OutTextCol")));
- </code>
- </example>
- </example>
-
<member name="WordEmbeddings">
<summary>
Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence vectors using a pre-trained model.