Kernel Methods for Minimally Supervised WSD

doi:10.1162/coli.2009.35.4.35407

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Kernel Methods for Minimally Supervised WSD

2009-12-01

Claudio Giuliano *

Alfio Gliozzo

Carlo Strapparava

DOI：10.1162/coli.2009.35.4.35407

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Abstract

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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.

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