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Posted to issues@spark.apache.org by "Alok Singh (JIRA)" <ji...@apache.org> on 2015/07/17 08:53:04 UTC
[jira] [Comment Edited] (SPARK-5571) LDA should handle text as well
[ https://issues.apache.org/jira/browse/SPARK-5571?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14630871#comment-14630871 ]
Alok Singh edited comment on SPARK-5571 at 7/17/15 6:52 AM:
------------------------------------------------------------
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
was (Author: aloknsingh):
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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