RTV: Tree kernels for thematic role classification

Daniele Pighin, Alessandro Moschitti, Roberto Basili

Research output: Contribution to conferencePaper

Abstract

We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. The resulting system has an F1 of 75.44 on the SemEval2007 closed task on semantic role labeling.

Original languageEnglish
Pages288-291
Number of pages4
Publication statusPublished - 1 Jan 2007
Event4th International Workshop on Semantic Evaluations, SemEval 2007 - Prague, Czech Republic
Duration: 23 Jun 200724 Jun 2007

Other

Other4th International Workshop on Semantic Evaluations, SemEval 2007
CountryCzech Republic
CityPrague
Period23/6/0724/6/07

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Cite this

Pighin, D., Moschitti, A., & Basili, R. (2007). RTV: Tree kernels for thematic role classification. 288-291. Paper presented at 4th International Workshop on Semantic Evaluations, SemEval 2007, Prague, Czech Republic.