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https://issues.apache.org/jira/browse/SPARK-20028?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15986646#comment-15986646
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Chenzhao Guo commented on SPARK-20028:
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N-gram is a popular concept in NLP field, while Spark currently doesn't support
using Hive UDAF GenericUDAFnGrams, which is actually a feature missing.
> Implement NGrams aggregate function
> -----------------------------------
>
> Key: SPARK-20028
> URL: https://issues.apache.org/jira/browse/SPARK-20028
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Chenzhao Guo
>
> This is the implementation of `ngrams` aggregate expression which is also
> implemented by Hive. It takes use of n-gram concept in natural language
> processing to understand texts.
> Currently, Spark doesn't support using Hive UDAF GenericUDAFnGrams, which is
> actually a feature missing.
> An n-gram is a contiguous subsequence of n item(s) drawn from a given
> sequence. This expression finds the k most frequent n-grams from one or more
> sequences.
> This expression has the pattern of : ngrams(children: Array[Array[String]](or
> Array[String]), n: Int, k: Int, accuracy: Int), it can be used in conjuction
> with `sentences` to split the column of String to Array. Among the
> parameters:
> Children indicates the 'given sequence' we collect n-grams from;
> N indicates n-gram's element number, size 1 is referred to as a "unigram",
> size 2 is a "bigram", size 3 is a "trigram"...
> K indicates top k;
> Accuracy is related to the memory used for frequency estimation, more memory
> will give more accurate frequency counts.
> A simple example:
> `SELECT ngrams(array("abc", "abc", "bcd", "abc", "bcd"), 2, 4);` will get
> `[{["abc","bcd"]:2.0},
> {["abc","abc"]:1.0},
> {["bcd","abc"]:1.0}]`. Because there are four 2-grams for the input which are
> `["abc", "abc"], ["abc", "bcd"], ["bcd", "abc"], ["abc", "bcd"]`, and
> `["abc", "bcd"]` occurs 2 times, the other two 2-grams occurs 1 time each,
> while `["abc","abc"]` is alphabetically before `["bcd","abc"]`, so the answer
> is like that.
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