Sounds like a good change, but I have two questions: will this affect the current APIs? Will people's maxent models still work if they are using a Maxent model now in a component that will soon require a Seq tagging model after the change?
On Mon, Jan 20, 2014 at 8:44 AM, Richard Eckart de Castilho < [email protected]> wrote: > Would it still be possible to use the current 1.5.x models with OpenNLP > after the change? > > -- Richard > > On 20.01.2014, at 07:48, Jörn Kottmann <[email protected]> wrote: > > > Hi all, > > > > in OpenNLP we have a couple of components which rely on sequence tagging. > > Right now they are using a normal classifier and search for a good > sequence via beam search. > > > > I would like to propose that we change that a bit, all components which > are based on sequence > > tagging should use a Sequence Classification Model instead of directly > using an > > Event Classification Model (currently named MaxentModel). > > > > The change will have two advantages, It will be possible to integrate ml > algorithm which operate on a sequence > > level (e.g. CRF) and it would be easy to exchange beam search against a > similar (maybe enhanced) algorithm. > > > > On the training side we already have support for training on sequences. > Anyway the current implementation is a bit > > unlucky because the sequence training class can only return an Event > Classification Model. I will change that so that > > a Sequence Classification Model has to be returned, and the Perceptron > Sequence Model will be returned as a > > Sequence Classification Model instead. > > > > Any thoughts? > > > > Jörn >
