The nature of statistical learning theory
The nature of statistical learning theory
Automatic labeling of semantic roles
Computational Linguistics
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Support Vector Learning for Semantic Argument Classification
Machine Learning
Using LTAG based features in parse reranking
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Semantic role labeling using different syntactic views
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Speeding up training with tree kernels for node relation labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Engineering of syntactic features for shallow semantic parsing
FeatureEng '05 Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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We present a method for recognizing semantic role arguments using a kernel on weighted marked ordered labeled trees (the WMOLT kernel). We extend the kernels on marked ordered labeled trees (Kazama and Torisawa, 2005) so that the mark can be weighted according to its importance. We improve the accuracy by giving more weights on subtrees that contain the predicate and the argument nodes with this ability. Although Kazama and Torisawa (2005) presented fast training with tree kernels, the slow classification during runtime remained to be solved. In this paper, we give a solution that uses an efficient DP updating procedure applicable in argument recognition. We demonstrate that the WMOLT kernel improves the accuracy, and our speed-up method makes the recognition more than 40 times faster than the naive classification.