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
