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From Jason Baldridge <jasonbaldri...@gmail.com>
Subject Re: using Scala for opennlp.ml
Date Tue, 10 Jan 2012 05:20:28 GMT
+1 to this in general, though I'm not into over-architecting things
initially. Would be great to get things humming and then start supporting
more pluggability.

On Sat, Jan 7, 2012 at 7:33 AM, Jörn Kottmann <kottmann@gmail.com> wrote:

> On 1/7/12 2:22 PM, Grant Ingersoll wrote:
>
>> Being able to take advantage of other classifiers seems like it would be
>> a really nice thing to be able to do.  I'd love to put OpenNLP over Mahout
>> or others.
>>
>> Besides, for testing purposes, if you could plugin the existing
>> capability versus your new rewrite (in Scala) then you could easily compare
>> the two.  I can't imagine the abstraction layer is more than a few
>> interfaces or abstract classes plus a bit of configuration/injection/fill
>> in the blank that allows one to specify the implementation.
>>
>
> Yes, we need plug-able classifiers and support for extensive
> modification/extension of
> our existing components. You are welcome to help us with that.
>
> One way of implementing this is to specify a (optional) factory class
> during training
> which is used to create a model (classifier). A second type of factory
> class could
> be specified to modify a component.
>
> These factory class names will be stored in our zip model package, and can
> then be used to instantiated the extensions which are necessary to run the
> component.
>
> The disadvantage of this approach is that it might not work well with OSGi.
> A big advantage is that OpenNLP itself will take care of configuring
> everything
> and the code needed to run an OpenNLP component is identical, even if the
> model
> uses "custom" extensions. These must only be on the class path.
>
> Jörn
>



-- 
Jason Baldridge
Associate Professor, Department of Linguistics
The University of Texas at Austin
http://www.jasonbaldridge.com
http://twitter.com/jasonbaldridge

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