A New Minimally-Supervised Framework for Domain Word Sense Disambiguation

We present a new minimally-supervised framework for performing domain-driven Word Sense Disambiguation (WSD). Glossaries for several domains are iteratively acquired from the Web by means of a bootstrapping technique. The acquired glosses are then used as the sense inventory for fully-unsupervised domain WSD. Our experiments, on new and gold-standard datasets, show that our wide-coverage framework enables high-performance results on dozens of domains at a coarse and fine-grained level.

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