[jira] [Commented] (SPARK-14864) [MLLIB] Implement Doc2Vec

["Vignesh Mohan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-14864?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16086869#comment-16086869 ] 

Vignesh Mohan commented on SPARK-14864:
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Since doc2vec is a very efficient way of representing features for texts, it would be much helpful if this is integrated with spark. I would like to know about this progress

> [MLLIB] Implement Doc2Vec
> -------------------------
>
>                 Key: SPARK-14864
>                 URL: https://issues.apache.org/jira/browse/SPARK-14864
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Peter Mountanos
>            Priority: Minor
>
> It would be useful to implement Doc2Vec, as described in the paper [Distributed Representations of Sentences and Documents|https://cs.stanford.edu/~quocle/paragraph_vector.pdf]. Gensim has an implementation [Deep learning with paragraph2vec|https://radimrehurek.com/gensim/models/doc2vec.html]. 
> Le & Mikolov show that when aggregating Word2Vec vector representations for a paragraph/document, it does not perform well for prediction tasks. Instead, they propose the Paragraph Vector implementation, which provides state-of-the-art results on several text classification and sentiment analysis tasks.



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