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From "Joern Kottmann (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (LEGAL-309) Apache OpenNLP wants to release models trained on Universal Dependency under AL 2.0
Date Fri, 19 May 2017 11:15:04 GMT

     [ https://issues.apache.org/jira/browse/LEGAL-309?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joern Kottmann updated LEGAL-309:
---------------------------------
    Description: 
The OpenNLP project develops statistical natural language processing software which needs
to be trained in order to produce a model that can be used to perform one of our supported
tasks such as part-of-speech tagging or lemmatization.

We would like to know if it would be possible to train models on data included in UD which
itself is licensed under various licenses and then release the trained models under AL 2.0.

If you go to [1] you can see a list of data files and their license.

Here is a list of the licenses:
CC BY 4.0
CC BY SA 4.0
CC BY-NC-SA 2.5, 3.0, 4.0 and without version
CC BY-NC-SA US 3.0
CC BY-SA 4.0 
GPL
LGPLLR

The models we would like to train on that data are:
- Part-of-Speech models (contains bigrams and a set of individual words of the training text)
- Lemmatizer (contains a set of individual words of the training text)

As far as we understand individual words or very short phrases extracted from a corpus are
not protected by its original copyright. The above licenses as far as we know don't forbid
to derive statistics from its content. 

[1] http://universaldependencies.org/

 


  was:
he OpenNLP project develops statistical natural language processing software which needs to
be trained in order to produce a model that can be used to perform one of our supported tasks
such as part-of-speech tagging or lemmatization.

We would like to know if it would be possible to train models on data included in UD which
itself is licensed under various licenses and then release the trained models under AL 2.0.

If you go to [1] you can see a list of data files and their license.

Here is a list of the licenses:
CC BY 4.0
CC BY SA 4.0
CC BY-NC-SA 2.5, 3.0, 4.0 and without version
CC BY-NC-SA US 3.0
CC BY-SA 4.0 
GPL
LGPLLR

The models we would like to train on that data are:
- Part-of-Speech models (contains bigrams and a set of individual words of the training text)
- Lemmatizer (contains a set of individual words of the training text)

As far as we understand individual words or very short phrases extracted from a corpus are
not protected by its original copyright. The above licenses as far as we know don't forbid
to derive statistics from its content. 

[1] http://universaldependencies.org/

 



> Apache OpenNLP wants to release models trained on Universal Dependency under AL 2.0
> -----------------------------------------------------------------------------------
>
>                 Key: LEGAL-309
>                 URL: https://issues.apache.org/jira/browse/LEGAL-309
>             Project: Legal Discuss
>          Issue Type: Question
>            Reporter: Joern Kottmann
>
> The OpenNLP project develops statistical natural language processing software which needs
to be trained in order to produce a model that can be used to perform one of our supported
tasks such as part-of-speech tagging or lemmatization.
> We would like to know if it would be possible to train models on data included in UD
which itself is licensed under various licenses and then release the trained models under
AL 2.0.
> If you go to [1] you can see a list of data files and their license.
> Here is a list of the licenses:
> CC BY 4.0
> CC BY SA 4.0
> CC BY-NC-SA 2.5, 3.0, 4.0 and without version
> CC BY-NC-SA US 3.0
> CC BY-SA 4.0 
> GPL
> LGPLLR
> The models we would like to train on that data are:
> - Part-of-Speech models (contains bigrams and a set of individual words of the training
text)
> - Lemmatizer (contains a set of individual words of the training text)
> As far as we understand individual words or very short phrases extracted from a corpus
are not protected by its original copyright. The above licenses as far as we know don't forbid
to derive statistics from its content. 
> [1] http://universaldependencies.org/
>  



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