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.

The Trento / IITP / Essex Submission to the CONLL Shared Task

Uryupina, Olga;Poesio, Massimo
2011-01-01

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.
2011
Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Stroudsburg, PA, USA
Association for Computational Linguistics (ACL)
9781937284084
Uryupina, Olga; S., Saha; A., Ekbal; Poesio, Massimo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/89863
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