Knowledge Lean Word-Sense Disambiguation

We present a corpus based approach to word sense disambiguation that only requires information that can be automatically extracted from untagged text We use unsupervised techniques to estimate the pa rameters of a model describing the conditional distri bution of the sense group given the known contextual features Both the EM algorithm and Gibbs Sampling are evaluated to determine which is most appropriate for our data We compare their disambiguation ac curacy in an experiment with thirteen di erent words and three feature sets Gibbs Sampling results in small but consistent improvement in disambiguation accu racy over the EM algorithm