The latest patch will be fine. Thanks. 2012. 9. 13. 오전 12:00에 "Jörn Kottmann" <kottm...@gmail.com>님이 작성:
> +1 to do that. > > We will name it MAXENT_QN_EXPERIMENTAL until > the current problems are solved. > > Do you want to update the patch file to your latest version > or should we commit the latest patch file attached to the issue? > > Jörn > > > On 09/12/2012 04:05 PM, Hyosup Shim wrote: > >> Hi, >> >> It's fine that pulling it in as an experimental feature. >> I think that it will be helpful that pulling it it, because following work >> will be >> more managable when the code is tracked by subversion. >> >> Thanks. >> >> >> 2012/9/12 Jörn Kottmann <kottm...@gmail.com> >> >> Hello, >>> >>> should we pull in the patch and mark it as experimental? >>> Any opinions about that? >>> >>> Thanks, >>> Jörn >>> >>> >>> On 08/26/2012 06:43 AM, Hyosup Shim wrote: >>> >>> Hi, >>>> >>>> I've been working on implmenting QNTrainer(L-bfgs maxent parameter >>>> estimator) in recent few weeks. >>>> >>>> My first implementation on the issue gave me about 0.80 precision on >>>> train/test set of PerceptronPrepAttach unit test. >>>> Since other existing estimators in OpenNLP showed nearly same precision >>>> on >>>> that test set, I did submitted the patch. >>>> >>>> But on CONLL02 test set Jorn gave me, QNTrainer got dissappointing >>>> result. >>>> (less than 0.05 in precision, 0.30 in recall) >>>> >>>> I tried to fix it, and failed. Could anyone give me a clue? >>>> >>>> OPENNLP-338 >>>> <https://issues.apache.org/****jira/browse/OPENNLP-338<https://issues.apache.org/**jira/browse/OPENNLP-338> >>>> <https:**//issues.apache.org/jira/**browse/OPENNLP-338<https://issues.apache.org/jira/browse/OPENNLP-338> >>>> > >>>> >>>> >
