Making large-scale support vector machine learning practical
Advances in kernel methods
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
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on 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
Information Processing and Management: an International Journal
Dependency Tree Kernels for Relation Extraction from Natural Language Text
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Semantic relation extraction with kernels over typed dependency trees
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A logic-based approach to relation extraction from texts
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Automatically structuring domain knowledge from text: An overview of current research
Information Processing and Management: an International Journal
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The automatic extraction of relations between entities expressed in natural language text is an important problem for IR and text understanding. In this paper we show how different kernels for parse trees can be combined to improve the relation extraction quality. On a public benchmark dataset the combination of a kernel for phrase grammar parse trees and for dependency parse trees outperforms all known tree kernel approaches alone suggesting that both types of trees contain complementary information for relation extraction.