Clause boundary recognition using support vector machines

Authors:
Hyun-Ju Lee;Seong-Bae Park;Sang-Jo Lee;Se-Young Park
Affiliations:
Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

This paper proposes a method for Korean clause boundary recognition. Clause boundary identification can be regarded as a three-class classification task, and it can be converted into a two-phase binary classification task. Then it is natural to apply SVMs to clause boundary recognition, since SVMs are basically binary classifiers. Specifically we first recognize the ending points of clauses, and then identify the starting points by considering the typological characteristics of Korean. In addition, since there is not a standard Korean corpus containing clause boundary information, we prepare a Korean clause identification dataset. In the evaluation, support vector machines yield the improvement of performance over memory-based learning or decision trees.