Text classification using string kernels
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
Description of the UMass system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
The NYU system for MUC-6 or where's the syntax?
MUC6 '95 Proceedings of the 6th conference on Message understanding
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
Closing the gap: learning-based information extraction rivaling knowledge-engineering methods
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
STAR '01 Proceedings of the ACL 2001 Workshop on Sharing Tools and Resources - Volume 15
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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Most traditional information extraction approaches are generative models that assume events exist in text in certain patterns and these patterns can be regenerated in various ways. These assumptions limited the syntactic clues being considered for finding an event and confined these approaches to a particular syntactic level. This paper presents a discriminative framework based on kernel SVMs that takes into account different levels of syntactic information and automatically identifies the appropriate clues. Kernels are used to represent certain levels of syntactic structure and can be combined in principled ways as input for an SVM. We will show that by combining a low level sequence kernel with a high level kernel on a GLARF dependency graph, the new approach outperformed a good rule-based system on slot filler detection for MUC-6.