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From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CTAKES-209) Simplify how SentenceDetector models are loaded
Date Fri, 14 Jun 2013 18:44:21 GMT

    [ https://issues.apache.org/jira/browse/CTAKES-209?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13683653#comment-13683653
] 

ASF subversion and git services commented on CTAKES-209:
--------------------------------------------------------

Commit 1493198 from chenpei@apache.org
[ https://svn.apache.org/r1493198 ]

CTAKES-209, CTAKES-210 -  With the new SentenceDetector working with OpenNLP 1.5, we can simplify
how models are loaded now.
- Deleted the SuffixMaxentModelResourceImpl and related classes. It's just a string to the
model file now.
- Deleted duplicated SentenceDectector.xml descriptors that were scattered.
- Added util in FileLocator.java to read in resources from InputStream instead of physical
path File.  Updated the SD to use this mechanism.
                
> Simplify how SentenceDetector models are loaded
> -----------------------------------------------
>
>                 Key: CTAKES-209
>                 URL: https://issues.apache.org/jira/browse/CTAKES-209
>             Project: cTAKES
>          Issue Type: Improvement
>          Components: ctakes-core
>    Affects Versions: 3.0-incubating
>            Reporter: Pei Chen
>            Priority: Minor
>             Fix For: 3.1
>
>
> Sentence Dectector is still using the OpenNLP 1.4 mechanism of loading it's model file.
> a.       Suggestion: Let's update it to use the new 1.5 way similar to POSTagger.  
> - Remove non longer required classes: SuffixMaxentModelResourceImpl, MaxentModelResource,
SuffixSensitiveGISModelReader, classes etc.
> I think this will keep it consistent with the other projects such as POSTagger.  
> There may have been a performance reason for shared resources, but this is a tiny model
compared to the others, and it's only done once during init time.  This should simplify the
code and it's references significantly. 

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