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https://issues.apache.org/jira/browse/SPARK-5874?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14367089#comment-14367089
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Abou Haydar Elias commented on SPARK-5874:
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The tokenizer as for now converts the input string to lowercase and then splits
it by white spaces only.
I suggest more flexibility for the Tokenizer pipeline stage. So we can
eventually add stemming and text analysis directly into the Tokenizer.
There are many post-tokenization steps that can be done, including (but not
limited to):
- [Stemming|http://en.wikipedia.org/wiki/Stemming] – Replacing words with their
stems. For instance with English stemming "bikes" is replaced with "bike"; now
query "bike" can find both documents containing "bike" and those containing
"bikes".
- Stop Words Filtering – Common words like "the", "and" and "a" rarely add any
value to a search. Removing them shrinks the index size and increases
performance. It may also reduce some "noise" and actually improve search
quality.
- [Text Normalization|http://en.wikipedia.org/wiki/Text_normalization] –
Stripping accents and other character markings can make for better searching.
- Synonym Expansion – Adding in synonyms at the same token position as the
current word can mean better matching when users search with words in the
synonym set.
so what do you think?
> How to improve the current ML pipeline API?
> -------------------------------------------
>
> Key: SPARK-5874
> URL: https://issues.apache.org/jira/browse/SPARK-5874
> Project: Spark
> Issue Type: Brainstorming
> Components: ML
> Reporter: Xiangrui Meng
> Assignee: Xiangrui Meng
> Priority: Critical
>
> I created this JIRA to collect feedbacks about the ML pipeline API we
> introduced in Spark 1.2. The target is to graduate this set of APIs in 1.4
> with confidence, which requires valuable input from the community. I'll
> create sub-tasks for each major issue.
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