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Computational Linguistics

Paola Merlo, Editor
December 2009, Vol. 35, No. 4, Pages 513-528
(doi: 10.1162/coli.2009.35.4.35407)
© 2009 Association for Computational Linguistics
Kernel Methods for Minimally Supervised WSD
Article PDF (200.3 KB)
Abstract

We present a semi-supervised technique for word sense disambiguation that exploits external knowledge acquired in an unsupervised manner. In particular, 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. The results show that the proposed approach achieves state-of-the-art performance on a wide range of lexical sample tasks and on the English all-words task of Senseval-3, although it uses a considerably smaller number of training examples than other methods.