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.

Domain Kernels for Word Sense Disambiguation

Gliozzo, Alfio Massimiliano;Giuliano, Claudio;Strapparava, Carlo
2005

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/3382
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