Word Sense Disambiguation Based on Multi-Classifier Decision

The problem of word sense disambiguation can be formalized to be a typical classify problem. The committee classifiers are trained by learning a small set of labeled examples, and then these classifiers are updated dynamically by unlabeled examples. The senses of ambiguous words are determined by combining the decision of the final committee classifiers. This approach avoids constructing large-scale sense-tagged corpus, and has higher accurate rate.