The new plugin facilitates three core applications in addition to export and evaluation features.
Regression, Classification, and Chunking applications are available to choose depending on the problem you are trying to solve and each come with and impressive array of algorithms to select and evaluate. Tuning models can be done by directly specifying an algorithm parameter, cost for example, specifying a Java Class for the algorithm to use, scaling or by amending your n-gram features used for classification or sequence features for chunking.
GATE have helpfully incorporated an array of algorithms from LIBSVM, WEKA and Mallet including CRF, Decision Trees, Max Entropy, Regression algorithms and WEKA's deep learning Multilayer Perceptron algorithm.
For those familiar with GATE it is all very straightforward just select the required PR (processing resource), set the run-time parameters and train a model on your reference corpus before evaluating performance and deploying the application on your specific task. For each run time parameter tool tips are provided to guide the user
We have just recently used the new Learning Framework to classify scientific abstracts in the field of health economics using the Classification PR and obtained very encouraging results when applying the Regression PR to customer surveys in order to understand the level of customer satisfaction.
For the economic evaluation work we used the WEKA J48 classification model as this gave better results in evaluation mode than LIBSVM generating the following document statistics:
Observed Agreement Cohen's Kappa Pi's Kappa
0.9545 0.9545 0.9416
This is a brilliant addition to the existing suite of plugins from GATE and you can either clone the latest version of the Learning Framework from Github at the following link and build it using Ant or alternatively activate the Learning Framework from the CREOLE Plugins Manager.
Learn more about training Machine Learning Models within GATE at the 9th GATE training course from 6-10 June 2016 at the University of Sheffield. https://gate.ac.uk/conferences/fig/fig9.html