Paper: Chunking With Support Vector Machines

ACL ID N01-1025
Title Chunking With Support Vector Machines
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization perfor- mance even with input data of high dimensional feature spaces. Furthermore, by the Kernel princi- ple, SVMs can carry out training with smaller com- putational overhead independent of their dimen- sionality. We apply weighted voting of 8 SVMs- based systems trained with distinct chunk repre- sentations. Experimental results show that our ap- proach achieves higher accuracy than previous ap- proaches.