Semantic role labeling using complete syntactic analysis

Mihai Surdeanu, Jordi Turmo

Research output: Contribution to conferencePaper

27 Scopus citations

Abstract

In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.

Original languageEnglish (US)
Pages221-224
Number of pages4
StatePublished - Dec 1 2005
Externally publishedYes
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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    Surdeanu, M., & Turmo, J. (2005). Semantic role labeling using complete syntactic analysis. 221-224. Paper presented at 9th Conference on Computational Natural Language Learning, CoNLL 2005, Ann Arbor, MI, United States.