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From "Alok Singh (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-5571) LDA should handle text as well
Date Fri, 17 Jul 2015 06:52:05 GMT

    [ https://issues.apache.org/jira/browse/SPARK-5571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14630871#comment-14630871
] 

Alok Singh commented on SPARK-5571:
-----------------------------------

Hi Feynman,

Sorry for the delay and gap, here at work , we had some training and few internal updates/changes
and was not able to respond.


Here are my thoughts , please comments

stemmer
------------
I think we will need the stemmer module too. I was thinking we can just create a wrapper over
the Lucene EnglishAnalyzer Or the OpenNLP stemmer. This can be seperate transformer  jira
under the 'ml' tag
Without this component, we will have a lot of edges and nodes in the created graphx.

Stopword
------------
we can support two ways
- in one user give the list of stop words
-in another, we calculate it using the idf with tfi-idf transformer. We could create the new
transformer which under the hood calls the tfi-df transformer with the filter range. This
can also be another transformer jira under 'ml' tag.

The  LDA.runText
----------------------------------
The core LDA.runText method can be under the mllib tag and can be easier with the assumption
that 
the input bag of words just need to be passed to a  CountVectorizer and then to LDA.run.
which will be implemented as per the description.

The complete pipeline
-----------------------------
User can create it's own pipeline using ml but I think we should create the TextLDA_Pipeline
which will combine the above steps together and put it under 'ml' tag jira


What are your thoughts [~josephkb] and [~fliang]

Alok


> LDA should handle text as well
> ------------------------------
>
>                 Key: SPARK-5571
>                 URL: https://issues.apache.org/jira/browse/SPARK-5571
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.3.0
>            Reporter: Joseph K. Bradley
>
> Latent Dirichlet Allocation (LDA) currently operates only on vectors of word counts.
 It should also supporting training and prediction using text (Strings).
> This plan is sketched in the [original LDA design doc|https://docs.google.com/document/d/1kSsDqTeZMEB94Bs4GTd0mvdAmduvZSSkpoSfn-seAzo/edit?usp=sharing].
> There should be:
> * runWithText() method which takes an RDD with a collection of Strings (bags of words).
 This will also index terms and compute a dictionary.
> * dictionary parameter for when LDA is run with word count vectors
> * prediction/feedback methods returning Strings (such as describeTopicsAsStrings, which
is commented out in LDA currently)



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