[jira] [Commented] (OPENNLP-1170) The inclusion of letter's mechanical meaning in NLP processes.

2018-04-11 Thread Jon O Labahn Jr (JIRA)

[ 
https://issues.apache.org/jira/browse/OPENNLP-1170?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16434249#comment-16434249
 ] 

Jon O Labahn Jr commented on OPENNLP-1170:
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Thank you for the redirection of effort, if indeed that will forward the
process...


Jon


-- 

Jon Labahn, Jr.

www.jonlabahnjr.info
www.conversio.me
www.conversio.us


> The inclusion of letter's mechanical meaning in NLP processes.
> --
>
> Key: OPENNLP-1170
> URL: https://issues.apache.org/jira/browse/OPENNLP-1170
> Project: OpenNLP
>  Issue Type: Improvement
>  Components: Language Detector, Machine Learning
>Affects Versions: 1.8.4
> Environment: The on the market app applying the technique is in Java 
> for Android.  Effectively the technique is not system specific.
>Reporter: Jon O Labahn Jr
>Priority: Minor
>  Labels: features
> Attachments: LNSR Presentation.pdf
>
>   Original Estimate: 1,344h
>  Remaining Estimate: 1,344h
>
> Using the LNSR ("Lenser") to Amplify Meaning
> The understanding that letters, numbers and natual sounds have no underlying 
> meaning is taken for granted by _virtually everyone_...
> With Letter/Numeral/Sonic Revealer, the capacity to use letters, numbers and 
> sounds-- with the defining "functional keywords"--can give greater depth to 
> documents and written works of all sorts!
> Imagine the increase in refinement, as the deep and fundemental mechanics of 
> written communication, are part of your organization's potental...
> Possible Applications:
> 1. Speed-Speak: Using single, or strings, of letters numbers and sounds in a 
> meaningful way...  For instance, cutting the sentence, "Maintain within 
> guidelines.", down to the letter, "a".
> 2. Program Computers Using English:. (Currently under development.)  
> Mathmatizing letter and number mechanics to program devices in common English.
> 3. A "Working" Word Definition; No Dictionary Required:
> Using the 26 mechanical kewords, get an approximate functional definition--in 
> English, Spanish, French--or any other language that uses the 26 letter 
> alphabet...(Most useful for _deepening_ one's understanding of expressions.)
> Goals:
> To expand the use of the LNSR app and techniques; to increase efficiency, 
> understanding and simplification in the above, and as yet undeveloped, 
> applications.



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[jira] [Commented] (OPENNLP-941) Add eval support to detokenizer

2018-04-11 Thread ASF GitHub Bot (JIRA)

[ 
https://issues.apache.org/jira/browse/OPENNLP-941?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16433798#comment-16433798
 ] 

ASF GitHub Bot commented on OPENNLP-941:


skrcode commented on issue #308: [OPENNLP-941] Added eval support to detokenizer
URL: https://github.com/apache/opennlp/pull/308#issuecomment-380428351
 
 
   @kottmann Done !


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> Add eval support to detokenizer
> ---
>
> Key: OPENNLP-941
> URL: https://issues.apache.org/jira/browse/OPENNLP-941
> Project: OpenNLP
>  Issue Type: Improvement
>  Components: Tokenizer
>Reporter: Joern Kottmann
>Priority: Major
>  Labels: help-wanted
>
> It would be nice to be able to evaluate a detokenizer on tokenizer training 
> data.



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[jira] [Commented] (OPENNLP-941) Add eval support to detokenizer

2018-04-11 Thread ASF GitHub Bot (JIRA)

[ 
https://issues.apache.org/jira/browse/OPENNLP-941?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16433616#comment-16433616
 ] 

ASF GitHub Bot commented on OPENNLP-941:


kottmann commented on issue #308: [OPENNLP-941] Added eval support to 
detokenizer
URL: https://github.com/apache/opennlp/pull/308#issuecomment-38032
 
 
   Sorry the delays here!


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> Add eval support to detokenizer
> ---
>
> Key: OPENNLP-941
> URL: https://issues.apache.org/jira/browse/OPENNLP-941
> Project: OpenNLP
>  Issue Type: Improvement
>  Components: Tokenizer
>Reporter: Joern Kottmann
>Priority: Major
>  Labels: help-wanted
>
> It would be nice to be able to evaluate a detokenizer on tokenizer training 
> data.



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