Thanks! I think that dictionary-based model will be enough for now. In
the future we will retrain the OpenNLP models for better performance.
Even so the basque Wikipedia is too small :(
best,
ander
az., 2014.eko urtren 29a 22:07(e)an, Joachim Daiber(e)k idatzi zuen:
Hi Ander,
the statistical backend currently only supports OpenNLP models. This
is simply because they were readily available. So from my point of
view there are 2 things you can do:
1. change Spotlight to additionally accept your tool (assuming it's
JVM based)
2. retrain your models with OpenNLP
But regardless, you do not need those necessarily. If you do not
provide the models to the training, the statistical backend will learn
a dictionary-based spotting model. Depending on the size of the
Wikipedia input, this should work equally well (if the Wikipedia is
too small, it might be a bit sparse).
Hope that helps,
Jo
On Wed, Jan 29, 2014 at 3:11 PM, [email protected]
<mailto:[email protected]> <[email protected]
<mailto:[email protected]>> wrote:
Hi spotlight users,
Our main idea is to apply NED in basque documents, for this
proposal, we
want to use the dbpedia spotlight statistical backend system.
We want to create a Spotlight model for Basque language, but we have a
"little" problem. We have seen that there isn't any openNLP model for
Basque. We have all the resources such as tokenizer, chuncker, POS
tagger, stopwords... but not any of the openNLP pre-trained models for
this language.
Our questions are:
Is there any other way to use this resources instead of using openNLP
models? For example, integrating our resources in the system code and
giving the output to dbpedia spotlight system (without openNLP
models).
Does someone done something like this before?
Or
Do we need to build an openNLP model compulsorily?
thanks in advance,
Ander
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