Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel methods for relation extraction
The Journal of Machine Learning Research
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Exploring syntactic features for relation extraction using a convolution tree kernel
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A Novel Composite Kernel Approach to Chinese Entity Relation Extraction
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Developing Position Structure-Based Framework for Chinese Entity Relation Extraction
ACM Transactions on Asian Language Information Processing (TALIP)
ACM Transactions on Asian Language Information Processing (TALIP)
A comparison of Chinese parsers for stanford dependencies
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
A structural approach to extracting Chinese position relations from web pages
Journal of Web Engineering
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In this paper, we mainly explore the effectiveness of two kernel-based methods, the convolution tree kernel and the shortest path dependency kernel, in which parsing information is directly applied to Chinese relation extraction on ACE 2007 corpus. Specifically, we explore the effect of different parse tree spans involved in convolution kernel for relation extraction. Besides, we experiment with composite kernels by combining the convolution kernel with feature-based kernels to study the complementary effects between tree kernel and flat kernels. For the shortest path dependency kernel, we improve it by replacing the strict same length requirement with finding the longest common subsequences between two shortest dependency paths. Experiments show kernel-based methods are effective for Chinese relation extraction.