We present a semi-supervised technique for word sense disambiguation that exploits external knowledge acquired in an unsupervised manner.
we use a combination of basic kernel functions to independently estimate syntagmatic and domain similarity, building a set of word-expert classifiers that share a common domain model acquired from a large corpus of unlabeled data.