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https://issues.apache.org/jira/browse/SPARK-8455?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14592508#comment-14592508
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Xiangrui Meng commented on SPARK-8455:
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It might be simpler if the output type is an array of strings, e.g., ["this
is", "is a", "a test"], instead of array of string arrays. After NGram, it is
easier for users to vectorize the output.
> Implement N-Gram Feature Transformer
> ------------------------------------
>
> Key: SPARK-8455
> URL: https://issues.apache.org/jira/browse/SPARK-8455
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Reporter: Feynman Liang
> Priority: Trivial
>
> N-grams are a NLP feature representation which generalize bag of words to
> include local context (the n-1 preceding words). We can implement N-grams in
> ML as a feature transformer (likely directly after tokenization).
> For example, "this is a test" should tokenize to ["this","is","a","test"],
> which upon applying a 2-gram feature transform should yield
> [["this","is"],["is","a"],["a","test"]].
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