Multi-metric optimization for coreference: the UniTN/IITP/Essex submission to the 2011 CoNLL Shared Task

  • Authors:
  • Olga Uryupina;Sriparna Saha;Asif Ekbal;Massimo Poesio

  • Affiliations:
  • University of Trento;Indian Institute of Technology Patna;Indian Institute of Technology Patna;University of Trento and University of Essex

  • Venue:
  • CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
  • Year:
  • 2011

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Abstract

Because there is no generally accepted metric for measuring the performance of anaphora resolution systems, a combination of metrics was proposed to evaluate submissions to the 2011 CONLL Shared Task (Pradhan et al., 2011). We investigate therefore Multi-objective function Optimization (moo) techniques based on Genetic Algorithms to optimize models according to multiple metrics simultaneously.