It would be interesting to compare the results of OpenNLP’s perceptron trained
models, GIS trained models, and a vanilla CRF implementation (i.e. not
specifically trained for a task). We can make a better decision on if we
should spend the effort to implement a CRF. Every once in a while we see
people ask “what can I do?”. Maybe the answer should be… given an
ObjectStream<Event> or DataIndexer, train a CRFModel that extends
AbstractModel. Your training class must extend AbstractEventTrainer and we
serializable using AbstractModelWriter
Just my 2 cents.
Daniel
On 2/7/17, 9:51 AM, "Damiano Porta" <damianopo...@gmail.com> wrote:
I have good results with perceptron, but +1 for CRF
2017-02-07 15:42 GMT+01:00 Russ, Daniel (NIH/CIT) [E] <dr...@mail.nih.gov>:
> Hi Jörn,
>
>
>
> I think the best entity recognition systems use CRF’s. At some point
> we might want to consider adding them. As you know, ME classifiers suffer
> from label bias problem (see Lafferty et. al<http://repository.upenn.
> edu/cgi/viewcontent.cgi?article=1162&context=cis_papers>.) CRF’s deal
> with that issue. I believe that perceptrons suffer from the same problem.
> If you think the results are better, I have no problem. I think that our
> long-term goal should be to add a CRF, and make it the default for the
> NameFinder.
>
>
>
> Daniel
>
>
>
>
>
> On 2/6/17, 12:40 PM, "Joern Kottmann" <kottm...@gmail.com> wrote:
>
>
>
> Hello all,
>
>
>
> I would like to propose to switch the default training algorithm from
>
> maxent gis to perceptron for the Name Finder. In all the data sets I
>
> tried perceptron performs better than maxent gis and I believe that
>
> would be a much more sensible default.
>
>
>
> A user can always override the default by providing the algorithms
>
> parameter for training.
>
>
>
> What do you think?
>
>
>
> Jörn
>
>
>
>
>
