{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:54:23Z","timestamp":1767034463245,"version":"3.41.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2008,11,1]],"date-time":"2008-11-01T00:00:00Z","timestamp":1225497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Transactions on Asian Language Information Processing"],"published-print":{"date-parts":[[2008,11]]},"abstract":"<jats:p>As a kind of Shallow Semantic Parsing, Semantic Role Labeling (SRL) is gaining more attention as it benefits a wide range of natural language processing applications. Given a sentence, the task of SRL is to recognize semantic arguments (roles) for each predicate (target verb or noun). Feature-based methods have achieved much success in SRL and are regarded as the state-of-the-art methods for SRL. However, these methods are less effective in modeling structured features. As an extension of feature-based methods, kernel-based methods are able to capture structured features more efficiently in a much higher dimension. Application of kernel methods to SRL has been achieved by selecting the tree portion of a predicate and one of its arguments as feature space, which is named as predicate-argument feature (PAF) kernel. The PAF kernel captures the syntactic tree structure features using convolution tree kernel, however, it does not distinguish between the path structure and the constituent structure. In this article, a hybrid convolution tree kernel is proposed to model different linguistic objects. The hybrid convolution tree kernel consists of two individual convolution tree kernels. They are a Path kernel, which captures predicate-argument link features, and a Constituent Structure kernel, which captures the syntactic structure features of arguments. Evaluations on the data sets of the CoNLL-2005 SRL shared task and the Chinese PropBank (CPB) show that our proposed hybrid convolution tree kernel statistically significantly outperforms the previous tree kernels. Moreover, in order to maximize the system performance, we present a composite kernel through combining our hybrid convolution tree kernel method with a feature-based method extended by the polynomial kernel. The experimental results show that the composite kernel achieves better performance than each of the individual methods and outperforms the best reported system on the CoNLL-2005 corpus when only one syntactic parser is used and on the CPB corpus when automated syntactic parse results and correct syntactic parse results are used respectively.<\/jats:p>","DOI":"10.1145\/1450295.1450298","type":"journal-article","created":{"date-parts":[[2008,12,3]],"date-time":"2008-12-03T21:56:04Z","timestamp":1228341364000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling"],"prefix":"10.1145","volume":"7","author":[{"given":"Wanxiang","family":"Che","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research"}]},{"given":"AiTi","family":"Aw","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research"}]},{"given":"ChewLim","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2008,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.3115\/980845.980860"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/234285.234289"},{"volume-title":"Proceedings of the 8th Conference on Natural Language Learning (CoNLL\u201904)","author":"Carreras X.","key":"e_1_2_1_3_1","unstructured":"Carreras , X. and M\u00e0rquez , L . 2004. Introduction to the CoNLL-2004 shared task: Semantic role labeling . In Proceedings of the 8th Conference on Natural Language Learning (CoNLL\u201904) . 89--97. Carreras, X. and M\u00e0rquez, L. 2004. Introduction to the CoNLL-2004 shared task: Semantic role labeling. In Proceedings of the 8th Conference on Natural Language Learning (CoNLL\u201904). 89--97."},{"volume-title":"Proceedings of the 9th Conference on Natural Language Learning (CoNLL\u201905)","author":"Carreras X.","key":"e_1_2_1_4_1","unstructured":"Carreras , X. and M\u00e0rquez , L . 2005. Introduction to the CoNLL-2005 shared task: Semantic role labeling . In Proceedings of the 9th Conference on Natural Language Learning (CoNLL\u201905) . 152--164. Carreras, X. and M\u00e0rquez, L. 2005. Introduction to the CoNLL-2005 shared task: Semantic role labeling. In Proceedings of the 9th Conference on Natural Language Learning (CoNLL\u201905). 152--164."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/974305.974323"},{"volume-title":"Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics (COLING-ACL\u201906)","author":"Che W.","key":"e_1_2_1_6_1","unstructured":"Che , W. , Zhang , M. , Liu , T. , and Li , S . 2006. A hybrid convolution tree kernel for semantic role labeling . In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics (COLING-ACL\u201906) . Sydney, Australia. Che, W., Zhang, M., Liu, T., and Li, S. 2006. A hybrid convolution tree kernel for semantic role labeling. In Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics (COLING-ACL\u201906). Sydney, Australia."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119199"},{"volume-title":"Proceedings of the 15th Annual Conference on Neutral Information Processing Systems (NIPS\u201901)","author":"Collins M.","key":"e_1_2_1_9_1","unstructured":"Collins , M. and Duffy , N . 2001. Convolution kernels for natural language . In Proceedings of the 15th Annual Conference on Neutral Information Processing Systems (NIPS\u201901) . Collins, M. and Duffy, N. 2001. Convolution kernels for natural language. In Proceedings of the 15th Annual Conference on Neutral Information Processing Systems (NIPS\u201901)."},{"volume-title":"An Introduction to Support Vector Machines","author":"Cristianini N.","key":"e_1_2_1_10_1","unstructured":"Cristianini , N. and Shawe-Taylor , J. 2000. 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Semantic role labeling system using maximum entropy classifier . In Proceedings of the 9th Conference on Natural Language Learning (CoNLL\u201905) . 189--192. Liu, T., Che, W., Li, S., Hu, Y., and Liu, H. 2005. Semantic role labeling system using maximum entropy classifier. In Proceedings of the 9th Conference on Natural Language Learning (CoNLL\u201905). 189--192."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244302760200687"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/972470.972475"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3115\/1218955.1218998"},{"volume-title":"Proceedings of the Workshop on Learning Structured Information for Natural Language Applications, 11th International Conference on European Association for Computational Linguistics (EACL\u201906)","author":"Moschitti A.","key":"e_1_2_1_23_1","unstructured":"Moschitti , A. , Pighin , D. , and Basili , R . 2006. 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