Single and multi-objective optimization for feature selection in anaphora resolution
Sriparna Saha, Asif Ekbal, Olga Uryupina, Massimo Poesio · 2011
There is no generally accepted met-ric for measuring the performance of anaphora resolution systems, and the ex-isting metrics—MUC, B3, CEAF, Blanc, among others—tend to reward signifi-cantly different behaviors. Systems op-timized according to one metric tend to perform poorly with respect to other ones, making it very difficult to compare anaphora resolution systems, as clearly shown by the results of the SEMEVAL 2010 Multilingual Coreference task. One so-lution would be to find a single com-pletely satisfactory metric, but it’s not clear whether this is possible and at any rate it is not going to happen any time soon. An alternative is to optimize mod-els according to multiple metrics simulta-neously. In this paper, we show, first of all, that this is possible to develop such models using Multi-objective Optimiza-tion (MOO) techniques based on Genetic Algorithms. Secondly, we show that op-timizing according to multiple metrics si-multaneously may result in better results with respect to each individual metric than optimizing according to that metric only. 1