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The Learning Framework is GATE's most recent machine learning plugin. 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.
As in the Batch Learning PR, the Learning Framework PR 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;
- Named entity recognition, which finds entity mentions, such as locations or persons, within the text.