Paper: Semantic Role Recognition Using Kernels On Weighted Marked Ordered Labeled Trees

ACL ID W06-2908
Title Semantic Role Recognition Using Kernels On Weighted Marked Ordered Labeled Trees
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2006
Authors

We present a method for recognizing se- mantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its im- portance. We improve the accuracy by giving more weights on subtrees that con- tain the predicate and the argument nodes with this ability. Although Kazama and Torisawa (2005) presented fast training with tree kernels, the slow classification during runtime remained to be solved. In this paper, we give a solution that uses an efficient DP updating procedure applica- ble in argument recognition. We demon- strate that the WMOLT kernel improves the accuracy, and our speed-up method makes the recognition more than 40 times...