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This paper proposes a novel approach to improve the kernel-based word sense disambiguation (WSD). We first explain why linear kernels are more suitable to WSD and many other natural language processing problems than translation-invariant kernels. Based on the linear kernel, two external knowledge sources are integrated. One comprises a set of linguistic rules to find the crucial features. For the other, a distributional similarity thesaurus is used to alleviate data sparseness by generalizing crucial features when they do not match the word-form exactly. The experiments show that we have outperformed the state-of-the-art system on the benchmark data from English lexical sample task of SemEval-2007 and the improvement is statistically significant.
Date of Conference: 19-22 Oct. 2008