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The Learning Framework is GATE's most recent machine learning plugin. It's still under active development, and undergoing some flux still, but stable enough to use. It offers a wider variety of more up to date ML algorithms than earlier machine learning plugins, currently supporting several Weka classification algorithms, various Mallet classification algorithms, Mallet's CRF implementation and LibSVM. It offers broadly the same functionality as the Batch Learning PR, with some differences--in addition to providing a broader range of algorithms, it is likely to be faster to train and apply under most circumstances, export to sparse ARFF format is included, and the interface design is a little different, offering more settings in the form of runtime parameters, and supporting multiple trained models in a more user-friendly way.
The Learning Framework implements different task modes:
- Classification, which simply assigns a class to each instance annotation. For example, each sentence might be classified as positive or negative;
- Sequence tagging, which finds mentions, such as locations or persons, within the text;
- Regression, which assigns a numerical target, and might be used to rank disambiguation candidates, for example.
These are provided in separate processing resources (PRs), with separate PRs for training and application and evaluation plugins for classification and regression. The plugin also includes an export PR, allowing GATE to be used to prepare feature files from textual data that can then be exported and used outside of GATE.
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