%0 Journal Article
	%A Sang-Soo Kim and  Seong-Bae Park and  Sang-Jo Lee
	%D 2007
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 1, 2007
	%T Resolving Dependency Ambiguity of Subordinate Clauses using Support Vector Machines 
	%U https://publications.waset.org/pdf/15230
	%V 1
	%X In this paper, we propose a method of resolving  dependency ambiguities of Korean subordinate clauses based on Support Vector Machines (SVMs). Dependency analysis of clauses is well known to be one of the most difficult tasks in parsing sentences, especially in Korean. In order to solve this problem, we assume that the dependency relation of Korean subordinate clauses is the dependency relation among verb phrase, verb and endings in the clauses. As a result, this problem is represented as a binary classification task. In order to apply SVMs to this problem, we selected two kinds of features: static and dynamic features. The experimental results on STEP2000 corpus show that our system achieves the accuracy of 73.5%. 
	%P 95 - 100