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From Thomas Ginter <thomas.gin...@utah.edu>
Subject Re: UIMAFit vs. LEO
Date Thu, 21 May 2015 15:19:05 GMT
Hi Peter,

That is an excellent question.  Leo was written with two goals in mind.  You have touched
on one of them.  

1.  Utilize UIMA-AS to lend scalability to our NLP processing.
2.  Facilitate the reuse of existing and third party code.

Obviously it was built as a means of programmatically creating and managing pipelines as UIMA-AS
services, as well as managing the client side of UIMA-AS, to facilitate the first goal.  Less
well known is that because of the second goal Leo allows you to either utilize the framework
to create the description for an analysis engine or aggregate engine (pipeline) but it also
allows you to import descriptors or description objects generated from other sources.  In
this way we can reuse modules, or even whole pipelines, like YTEX and cTAKES, without needing
to have access to the source code to insert the appropriate metadata.  If you wanted to use
UIMAFit to generate the description object for an analysis engine and then insert that analysis
engine into your Leo pipeline it is also relatively easy to do so. 


Thomas Ginter

> On May 19, 2015, at 18:49, Petr Baudis <pasky@ucw.cz> wrote:
>  Hi!
> On Thu, May 14, 2015 at 05:44:12PM +0000, Thomas Ginter wrote:
>> There is also Leo which allows you to programmatically create pipelines, launch them
as UIMA-AS services, and manage types systems and clients without having to touch any descriptor
files.  You can find documentation at the site below:
>> http://department-of-veterans-affairs.github.io/Leo/userguide.html
>  I'm wondering how does UIMAFit and LEO fit together.  My impression
> right now is:
>  * They both have the same goal.
>  * Mixing them in the same pipeline might get messy(?)
>  * LEO advantage is that it seamlessly works with UIMA-AS (in fact it's
>    built around UIMA-AS).
>  * UIMAFit advantage is (if nothing else) vastly wider ecosystem.
>  Did I get this about right?
>  Thanks,
> 				Petr Baudis

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