Word sense disambiguation using the classification information model: Experimental results on the SENSEVAL workshop

A Classification Information Model is a pattern classification model. The model decides the proper class of an input instance by integrating individual decisions, each of which is made with each feature in the pattern. Each individual decision is weighted according to the distributional property of the feature deriving the decision. An individual decision and its weight are represented as classification information which is extracted from the training instances. In the word sense disambiguation based on the model, the proper sense of an input instance is determined by the weighted sum of whole individual decisions derived from the features contained in the instance.