Structured Learning for Semantic Role Labeling

  • Danilo Croce
  • Roberto Basili
Conference paper

DOI: 10.1007/978-3-642-23954-0_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6934)
Cite this paper as:
Croce D., Basili R. (2011) Structured Learning for Semantic Role Labeling. In: Pirrone R., Sorbello F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science, vol 6934. Springer, Berlin, Heidelberg

Abstract

The use of complex grammatical features in statistical language learning assumes the availability of large scale training data and good quality parsers, especially for language different from English. In this paper, we show how good quality FrameNet SRL systems can be obtained, without relying on full syntactic parsing, by backing off to surface grammatical representations and structured learning. This model is here shown to achieve state-of-art results in standard benchmarks, while its robustness is confirmed in poor training conditions, for a language different for English, i.e. Italian.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Danilo Croce
    • 1
  • Roberto Basili
    • 1
  1. 1.Department of Enterprise EngineeringUniversity of Roma, Tor VergataRomaItaly

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