TY - CONF
T1 - Discriminative slot detection using kernel methods
AU - Zhao, Shubin
AU - Meyers, Adam
AU - Grishman, Ralph
N1 - Funding Information:
This research was supported in part by the Defense Advanced Research Projects Agency as part of the TIDES program, under Grant N66001-001-1-8917 from the Space and Naval Warfare Systems Center, San Diego, and by the National Science Foundation under Grant ITS-0325657. This paper does not necessarily reflect the position of the U.S. Government.
Funding Information:
This research was supported in part by the Defense Advanced Research Projects Agency as part of the TIDES program, under Grant N66001-001-1-8917 from the Space and Naval Warfare Systems Center, San Diego, and by the National Science Foundation under Grant ITS-0325657.
Publisher Copyright:
© 2004 COLING 2004 - Proceedings of the 20th International Conference on Computational Linguistics. All rights reserved.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
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M3 - Paper
AN - SCOPUS:85037065255
T2 - 20th International Conference on Computational Linguistics, COLING 2004
Y2 - 23 August 2004 through 27 August 2004
ER -