[jira] Updated: (UIMA-1833) Create an AE for training the HMM Tagger models

["Marshall Schor (JIRA)" <de...@uima.apache.org>]

     [ https://issues.apache.org/jira/browse/UIMA-1833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Marshall Schor updated UIMA-1833:
---------------------------------

    Fix Version/s: 2.3.1
                       (was: 2.3CE)

> Create an AE for training the HMM Tagger models
> -----------------------------------------------
>
>                 Key: UIMA-1833
>                 URL: https://issues.apache.org/jira/browse/UIMA-1833
>             Project: UIMA
>          Issue Type: Improvement
>          Components: Sandbox-Tagger
>         Environment: OS:
> Debian Linux Squeeze 64bits
> JVM:
> java version "1.6.0_20"
> Java(TM) SE Runtime Environment (build 1.6.0_20-b02)
> Java HotSpot(TM) 64-Bit Server VM (build 16.3-b01, mixed mode)
>            Reporter: Fabien POULARD
>            Assignee: Fabien POULARD
>            Priority: Minor
>             Fix For: 2.3.1
>
>         Attachments: model-trainer-ae.patch
>
>
> There is a class to train a model for the HMM Tagger out of a corpus. However, this is a standalone application that does not take advantage of the UIMA capabilities. It would be better to train such a model thanks to an analysis engine.
> A training CPE would be like :
>  1- a collection reader loading the gold standard corpus
>  2- the HMM Tagger model trainer analysis engine that would browse some specific annotation, extract the material to feed the learning algorithm and finally export a model file.

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