Ooops i'm really sorry...I am using the regular evaluator not the
cross-validator...The previous message should have been about the
standard evaluator...My bad! There is simply no point in using the
cross-validator with the dictionary - there is nothing to train! I
apologise for the mistake it wont happen again! The thing is i'm pretty
stressed out!
Jim
On 13/03/12 16:51, Jörn Kottmann wrote:
You only do cross validation because you need to take some data out to
train your model on. That is why you can pass in all the training
parameters
to the TokenNameFinderCrossValidator.
Maybe I am mistaken but it cannot take a TokenNameFinder object as an
argument, right?
Did you sub-classed it?
Anyway since the DictionaryNameFinder cannot be trained you should
just use the evaluators they are simpler and give you the same result.
Jörn
On 03/13/2012 05:34 PM, Jim - FooBar(); wrote:
Hey guys,
First of all i can imagine you must be sick and tired of me reporting
a bug or improvement every single day!That is the nature of
open-source though isn't it? :-)
Today's issue came literally out of nowhere! Again it has to do with
cross-validation but with the DictionaryNameFinder this time - NOT
the maxent model...Ok, here goes:
Basically, both the maxentNameFinder and the DictionaryNameFinder can
do NER. Also both classes implement TokenNameFinder so from Java's
perspective either can be passed as argument to the
TokenNameFinderCrossValidator constructor...However, i tried doing
that this morning, in an effort to get some numbers my dictionary,
and all i get is 0 precision, 0 recall and -1 FMeasure, regardless of
finding loads of drugs! I think (not 100% sure) the problem is that
the CrossValidator expects annotated text (in order to verify) but
the DictionaryNameFinder can only be deployed on un-annotated
text...To be honest i don't see any other reason why it won't use the
dictionary instead of the model since both classes conform to the
same interface - otherwise Java would complain!
Any ideas?
Jim
p.s: i 'm not sure if i can call this a bug or a massive
improvement...it all started when i started thinking how i can
evaluate my trained model when it joins forces with the
dictionary...i can see with my eyes there is some improvement but it
is crucial that i get some numbers...
