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.
word sense disambiguation kernel based method support vector machine
Peng Jin Fuxin Li Danqing Zhu Yunfang Wu Shiwen Yu
Institute of Computational Linguistics,Peking University Beijing,China Institute of Automation,Chinese Academy of Sciences Beijing,China
国际会议
北京
英文
2008-10-19（万方平台首次上网日期，不代表论文的发表时间）