A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
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
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Learning for information extraction: from named entity recognition and disambiguation to relation extraction
Syntactic kernels for natural language learning: the semantic role labeling case
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Syntactic tree kernels for event-time temporal relation learning
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Using syntactic-based kernels for classifying temporal relations
Journal of Computer Science and Technology - Special issue on natural language processing
A combination of topic models with max-margin learning for relation detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
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Relation extraction is a challenging task in natural language processing. Syntactic features are recently shown to be quite effective for relation extraction. In this paper, we generalize the state of the art syntactic convolution tree kernel introduced by Collins and Duffy. The proposed generalized kernel is more flexible and customizable, and can be conveniently utilized for systematic generation of more effective application specific syntactic sub-kernels. Using the generalized kernel, we will also propose a number of novel syntactic sub-kernels for relation extraction. These kernels show a remarkable performance improvement over the original Collins and Duffy kernel in the extraction of ACE-2005 relation types.