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 parameters of a model describing the conditional distribution 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 accuracy in an experiment with thirteen different words and three feature sets. Gibbs Sampling results in small but consistent improvement in disambiguation accuracy over the EM algorithm.

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