[jira] [Issue Comment Deleted] (SPARK-5571) LDA should handle text as well

["Alok Singh (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-5571?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Alok Singh updated SPARK-5571:
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    Comment: was deleted

(was: Ok it sounds good.

Stemmer:We have one scala stemmer in scalanlp%chalk https://github.com/scalanlp/chalk/tree/master/src/main/scala/chalk/text/analyze
which can easily copied (as it is apache project) and is in scala too.
I think this will be better alternative than lucene englishAnalyzer or opennlp.
Note: we already use the scalanlp%breeze via the maven dependency so I think adding scalanlp%chalk dependency is also the options. But as you had said we can copy the code as it is small. 


LDA.runText:sounds good. About the design doc, I think steps would be 
tokenize, stopword, stem, text2count, LDA.run(), return describeTopicAsString (which will be concat of the top stemmed words in the topic. ) 

Pipeline:I agree with the idea of the pipeline api can be added later and user can always use the LDA.runText from mllib. So we can just add a few more dependent jiras .Since 1.5 release has some time, we can have this feature implemented without the pipeline for the 1.5 release. 

)

> 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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