Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42149
Title: A grammar-driven convolution tree kernel for semantic role classification
Authors: Zhang, M.
Che, W.
Aw, A.T.
Tan, C.L. 
Zhou, G.
Liu, T.
Li, S.
Issue Date: 2007
Source: Zhang, M.,Che, W.,Aw, A.T.,Tan, C.L.,Zhou, G.,Liu, T.,Li, S. (2007). A grammar-driven convolution tree kernel for semantic role classification. ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics : 200-207. ScholarBank@NUS Repository.
Abstract: Convolution tree kernel has shown promising results in semantic role classification. However, it only carries out hard matching, which may lead to over-fitting and less accurate similarity measure. To remove the constraint, this paper proposes a grammardriven convolution tree kernel for semantic role classification by introducing more linguistic knowledge into the standard tree kernel. The proposed grammar-driven tree kernel displays two advantages over the previous one: 1) grammar-driven approximate substructure matching and 2) grammardriven approximate tree node matching. The two improvements enable the grammardriven tree kernel explore more linguistically motivated structure features than the previous one. Experiments on the CoNLL-2005 SRL shared task show that the grammardriven tree kernel significantly outperforms the previous non-grammar-driven one in SRL. Moreover, we present a composite kernel to integrate feature-based and tree kernel-based methods. Experimental results show that the composite kernel outperforms the previously best-reported methods. © 2007 Association for Computational Linguistics.
Source Title: ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics
URI: http://scholarbank.nus.edu.sg/handle/10635/42149
ISBN: 9781932432862
Appears in Collections:Staff Publications

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