Exploiting External Knowledge Sources to Improve Kernel-based Word Sense Disambiguation

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.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000266767300034&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)