Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Kernel-based semantic relation detection and classification via enriched parse tree structure
Journal of Computer Science and Technology - Special issue on natural language processing
ACM Transactions on Asian Language Information Processing (TALIP)
Tree kernel-based protein-protein interaction extraction from biomedical literature
Journal of Biomedical Informatics
Event argument extraction based on CRF
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
| Hi-index | 0.00 |
Convolution tree kernel has shown promising results in semantic role labeling (SRL). However, this kernel does not consider much linguistic knowledge in kernel design and only performs hard matching between subtrees. To overcome these constraints, this paper proposes a grammar-driven convolution tree kernel for SRL by introducing more linguistic knowledge. Compared with the standard convolution tree kernel, the proposed grammar-driven kernel has two advantages: 1) grammar-driven approximate substructure matching, and 2) grammar-driven approximate tree node matching. The two approximate matching mechanisms enable the proposed kernel to better explore linguistically motivated structured knowledge. Experiments on the CoNLL-2005 SRL shared task and the PropBank I corpus show that the proposed kernel outperforms the standard convolution tree kernel significantly. Moreover, we present a composite kernel to integrate a feature-based polynomial kernel and the proposed grammar-driven convolution tree kernel for SRL. Experimental results show that our composite kernel-based method significantly outperforms the previously best-reported ones.