senselearner-2.0
- SenseLearner is a system that attempts to disambiguate all open class words in any given text. It can be thought of as a minimally supervised WSD algorithm, in that it uses a small data set for training purposes. The algorithm does not need a separate classifier for each word to be disambiguated, but instead it learns global models for word categories. The current distribution comes with four models - for the various parts of speech. The implementation is however meant to be flexible, so that new models can be easily implemented and added to SenseLearner.