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https://issues.apache.org/jira/browse/SPARK-10105?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14702412#comment-14702412
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Apache Spark commented on SPARK-10105:
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User 'tmnd1991' has created a pull request for this issue:
https://github.com/apache/spark/pull/8301
> Adding most k frequent words parameter to Word2Vec implementation
> -----------------------------------------------------------------
>
> Key: SPARK-10105
> URL: https://issues.apache.org/jira/browse/SPARK-10105
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Antonio Murgia
> Priority: Minor
> Labels: mllib, top-k, word2vec
>
> When training Word2Vec on a really big dataset, it's really hard to evaluate
> the right minCount parameter, it would really help having a parameter to
> choose how many words you want to be in the vocabulary.
> Furthermore, the original Word2Vec paper, state that they took into account
> the most frequent 1M words.
>
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