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World Sense Disambiguation using Multiple Feature Decision Lists  

서희철 (고려대학교 컴퓨터학과)
임해창 (고려대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.7_8, 2003 , pp. 659-671 More about this Journal
Abstract
This paper proposes a method of disambiguating the senses of words using decision lists, which consists of rules with confidence values. The rule of decision list is composed of a boolean function(=precondition) and a class(=sense). Decision lists classify the instance using the rule with the highest confidence value that is matched with it. Previous work disambiguated the senses using single feature decision lists, whose boolean function was composed of only one feature. However, this approach can be affected more severely by data sparseness problem and preprocessing errors. Hence, we propose multiple feature decision lists that have the boolean function consisting of more than one feature in order to identify the senses of words. Experiments are performed with 1 sense tagged corpus in Korean and 5 sense tagged corpus in English. The experimental results show that multiple feature decision lists are more effective than single feature decision lists in disambiguating senses.
Keywords
multiple feature decision lists; word sense disambiguation;
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