Semantic dependency parsing of NomBank and PropBank: an efficient integrated approach via a large-scale feature selection

  • Authors:
  • Hai Zhao;Wenliang Chen;Chunyu Kit

  • Affiliations:
  • City University of Hong Kong, Kowloon, Hong Kong, China;National Institute of Information and Communications Technology, Soraku-gun, Kyoto, Japan;City University of Hong Kong, Kowloon, Hong Kong, China

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
  • Year:
  • 2009

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Abstract

We present an integrated dependency-based semantic role labeling system for English from both NomBank and PropBank. By introducing assistant argument labels and considering much more feature templates, two optimal feature template sets are obtained through an effective feature selection procedure and help construct a high performance single SRL system. From the evaluations on the date set of CoNLL-2008 shared task, the performance of our system is quite close to the state of the art. As to our knowledge, this is the first integrated SRL system that achieves a competitive performance against previous pipeline systems.