Semantic role labeling using libSVM

Necati Ercan Ozgencil; Nancy McCracken

doi:10.3115/1706543.1706582

Semantic role labeling using libSVM

Necati Ercan Ozgencil, Nancy McCracken

School of Information Studies

Research output: Contribution to conference › Paper › peer-review

5 Scopus citations

Abstract

We describe a system for the CoNLL- 2005 shared task of Semantic Role Labeling. The system implements a two-layer architecture to first identify the arguments and then to label them for each predicate. The components are implemented as SVM classifiers using libSVM. Features were adapted and tuned for the system, including a reduced set for the identifier classifier. Experiments were conducted to find kernel parameters for the Radial Basis Function (RBF) kernel. An algorithm was defined to combine the results of the argument labeling classifier according to the constraints of the argument labeling problem.

Original language	English (US)
Pages	205-208
Number of pages	4
DOIs	https://doi.org/10.3115/1706543.1706582
State	Published - 2005
Event	9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States Duration: Jun 29 2005 → Jun 30 2005

Other

Other	9th Conference on Computational Natural Language Learning, CoNLL 2005
Country/Territory	United States
City	Ann Arbor, MI
Period	6/29/05 → 6/30/05

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

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10.3115/1706543.1706582

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AU - McCracken, Nancy

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AB - We describe a system for the CoNLL- 2005 shared task of Semantic Role Labeling. The system implements a two-layer architecture to first identify the arguments and then to label them for each predicate. The components are implemented as SVM classifiers using libSVM. Features were adapted and tuned for the system, including a reduced set for the identifier classifier. Experiments were conducted to find kernel parameters for the Radial Basis Function (RBF) kernel. An algorithm was defined to combine the results of the argument labeling classifier according to the constraints of the argument labeling problem.

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ER -

Semantic role labeling using libSVM

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