Kernel methods for minimally supervised wsd

  • Authors:
  • Claudio Giuliano;Alfio Massimiliano Gliozzo;Carlo Strapparava

  • Affiliations:
  • -;-;-

  • Venue:
  • Computational Linguistics
  • Year:
  • 2009

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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.