Domain kernels for word sense disambiguation

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

  • Affiliations:
  • ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, Trento, Italy;ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, Trento, Italy;ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, Trento, Italy

  • Venue:
  • ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2005

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Abstract

In this paper we present a supervised Word Sense Disambiguation methodology, that exploits kernel methods to model sense distinctions. In particular a combination of kernel functions is adopted to estimate independently both syntagmatic and domain similarity. We defined a kernel function, namely the Domain Kernel, that allowed us to plug "external knowledge" into the supervised learning process. External knowledge is acquired from unlabeled data in a totally unsupervised way, and it is represented by means of Domain Models. We evaluated our methodology on several lexical sample tasks in different languages, outperforming significantly the state-of-the-art for each of them, while reducing the amount of labeled training data required for learning.