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      Multi-argument classification for semantic role labeling

      Lin, Chi-San Althon; Smith, Tony C.
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      Multi-Argument Classification.pdf
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       icgl.ctl.cityu.edu.hk
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      Lin, C.-S. A., & Smith, T. C. (2008). Multi-Argument classification for semantic role labeling. In Proceedings of the First International Workshop on Automated Syntactic Annotations for Interoperable Language Resources, Hong Kong, 8 January 2008 (pp. 53-60).
      Permanent Research Commons link: https://hdl.handle.net/10289/8103
      Abstract
      This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using standard evaluation metrics show that multi-argument classification, archieving 76.60 in F₁ measurement on WSJ 23, outperforms existing systems that use a single parse tree for the CoNLL 2005 shared task data. This paper also describes ways to significantly increase the speed of multi-argument classification, making it suitable for real-time language processing tasks that require semantic role labelling.
      Date
      2008
      Type
      Conference Contribution
      Publisher
      City University of Hong Kong
      Collections
      • Computing and Mathematical Sciences Papers [1454]
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