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From "Qian Huang (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-4036) Add Conditional Random Fields (CRF) algorithm to Spark MLlib
Date Tue, 16 Feb 2016 04:47:18 GMT

    [ https://issues.apache.org/jira/browse/SPARK-4036?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15148099#comment-15148099
] 

Qian Huang commented on SPARK-4036:
-----------------------------------

Hi, I have created a spark package, http://spark-packages.org/package/hqzizania/crf-spark.
I co-work on it with hujiayin.

[~josephkb] It can be said that this package is a spark-based re-implementation of crf++.
It has the same limit as crf++ has, like feature generator design and only for "segmenting/labeling
sequential data". But it basically meets the requirement of NLP and can run in parallel for
big data.

Welcome to try it. If you encounter bugs, feel free to submit an issue or pull request.




> Add Conditional Random Fields (CRF) algorithm to Spark MLlib
> ------------------------------------------------------------
>
>                 Key: SPARK-4036
>                 URL: https://issues.apache.org/jira/browse/SPARK-4036
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Guoqiang Li
>            Assignee: Kai Sasaki
>         Attachments: CRF_design.1.pdf, crf-spark.zip, dig-hair-eye-train.model, features.hair-eye,
sample-input, sample-output
>
>
> Conditional random fields (CRFs) are a class of statistical modelling method often applied
in pattern recognition and machine learning, where they are used for structured prediction.

> The paper: 
> http://www.seas.upenn.edu/~strctlrn/bib/PDF/crf.pdf



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