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 fullyunsupervised domain WSD. Our experiments, on new and gold-standard datasets, show that our wide-coverage framework enables highperformance results on dozens of domains at a coarse and fine-grained level.
REF attempted to performing domain-driven WSD by a pattern-based method with minimally-supervised framework.
A New Minimally-Supervised Framework for Domain Word Sense Disambiguation
EMNLP
Computer Science