TY - JFULL AU - Sang-Soo Kim and Seong-Bae Park and Sang-Jo Lee PY - 2007/2/ TI - Resolving Dependency Ambiguity of Subordinate Clauses using Support Vector Machines T2 - International Journal of Computer and Information Engineering SP - 94 EP - 100 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/15230 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 1, 2007 N2 - 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%. ER -