A Robust Geometric Model for Argument Classification

  • Cristina Giannone
  • Danilo Croce
  • Roberto Basili
  • Diego De Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5883)


Argument classification is the task of assigning semantic roles to syntactic structures in natural language sentences. Supervised learning techniques for frame semantics have been recently shown to benefit from rich sets of syntactic features. However argument classification is also highly dependent on the semantics of the involved lexicals. Empirical studies have shown that domain dependence of lexical information causes large performance drops in outside domain tests. In this paper a distributional approach is proposed to improve the robustness of the learning model against out-of-domain lexical phenomena.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cristina Giannone
    • 1
  • Danilo Croce
    • 1
  • Roberto Basili
    • 1
  • Diego De Cao
    • 1
  1. 1.University of Roma Tor VergataRomaItaly

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