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https://issues.apache.org/jira/browse/SPARK-5571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14633131#comment-14633131
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Alok Singh commented on SPARK-5571:
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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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