Paper: A Grammar-driven Convolution Tree Kernel for Semantic Role Classification

ACL ID P07-1026
Title A Grammar-driven Convolution Tree Kernel for Semantic Role Classification
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Convolution tree kernel has shown promis- ing results in semantic role classification. However, it only carries out hard matching, which may lead to over-fitting and less ac- curate similarity measure. To remove the constraint, this paper proposes a grammar- driven convolution tree kernel for semantic role classification by introducing more lin- guistic knowledge into the standard tree kernel. The proposed grammar-driven tree kernel displays two advantages over the pre- vious one: 1) grammar-driven approximate substructure matching and 2) grammar- driven approximate tree node matching. The two improvements enable the grammar- driven tree kernel explore more linguistically motivated structure features than the previ- ous one. Experiments on the CoNLL-2005 SRL shared task show that the gramm...