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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. . | A Grammar-driven Convolution Tree Kernel for Semantic Role Classification Min ZHANG1 Wanxiang CHE2 Ai Ti AW1 Chew Lim TAN3 Guodong ZHoU1 4 Ting LIU2 Sheng LI2 institute for Infocomm Research mzhang aaiti @i2r.a-star.edu.sg 3National University of Singapore tancl@comp.nus.edu.sg 2Harbin Institute of Technology car tliu @ir.hit.edu.cn lisheng@hit.edu.cn 4 Soochow Univ. China 215006 gdzhou@suda.edu.cn 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 grammar-driven 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 grammar-driven approximate tree node matching. The two improvements enable the grammar-driven tree kernel explore more linguistically motivated structure features than the previous one. Experiments on the CoNLL-2005 SRL shared task show that the grammar-driven 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. 1 Introduction Given a sentence the task of Semantic Role Label- ing SRL consists of analyzing the logical forms 200 expressed by some target verbs or nouns and some constituents of the sentence. In particular for each predicate target verb or noun all the constituents in the sentence which fill semantic arguments roles of the predicate have to be recognized. Typical semantic roles include Agent Patient Instrument etc. and also adjuncts such as Locative Temporal Manner and Cause etc. Generally semantic role .