Semantic role labeling using libSVM

Necati Ercan Ozgencil, Nancy McCracken

Research output: Contribution to conferencePaper

4 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 languageEnglish (US)
Pages205-208
Number of pages4
DOIs
StatePublished - Jan 1 2005
Event9th Conference on Computational Natural Language Learning, CoNLL 2005 - Ann Arbor, MI, United States
Duration: Jun 29 2005Jun 30 2005

Other

Other9th Conference on Computational Natural Language Learning, CoNLL 2005
CountryUnited States
CityAnn Arbor, MI
Period6/29/056/30/05

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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    Ozgencil, N. E., & McCracken, N. (2005). Semantic role labeling using libSVM. 205-208. Paper presented at 9th Conference on Computational Natural Language Learning, CoNLL 2005, Ann Arbor, MI, United States. https://doi.org/10.3115/1706543.1706582