Paper: A Study On Convolution Kernels For Shallow Statistic Parsing

ACL ID P04-1043
Title A Study On Convolution Kernels For Shallow Statistic Parsing
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors

In this paper we have designed and experi- mented novel convolution kernels for automatic classi cation of predicate arguments. Their main property is the ability to process struc- tured representations. Support Vector Ma- chines (SVMs), using a combination of such ker- nels and the at feature kernel, classify Prop- Bank predicate arguments with accuracy higher than the current argument classi cation state- of-the-art. Additionally, experiments on FrameNet data have shown that SVMs are appealing for the classi cation of semantic roles even if the pro- posed kernels do not produce any improvement.