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A Generative Model for Semantic Role Labeling

  • Cynthia A. Thompson
  • Roger Levy
  • Christopher D. Manning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this task, our model does not assume that the frame of a sentence is known, and is able to identify null-instantiated roles, which commonly occur in our corpus and whose identification is crucial to natural language interpretation.

Keywords

Machine Translation Semantic Role Statistical Machine Translation Lexical Unit Natural Language Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Cynthia A. Thompson
    • 1
  • Roger Levy
    • 2
  • Christopher D. Manning
    • 2
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA
  2. 2.Departments of Linguistics and Computer ScienceStanford UniversityStanfordUSA

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