A State-of-the-Art Mention-Pair Model for Coreference Resolution
Olga Uryupina, Alessandro Moschitti · 2015
Most recent studies on coreference resolu-tion advocate accurate yet relatively com-plex models, relying on, for example, entity-mention or graph-based representations. As it has been convincingly demonstrated at the recent CoNLL 2012 shared task, such algo-rithms considerably outperform popular basic approaches, in particular mention-pair mod-els. This study advocates a novel approach that keeps the simplicity of a mention-pair framework, while showing state-of-the-art re-sults. Apart from being very efficient and straightforward to implement, our model fa-cilitates experimental work on the pairwise classifier, in particular on feature engineering. The proposed model achieves the performance level of up to 61.82 % (MELA F, v4 scorer) on the CoNLL test data, on par with complex state-of-the-art systems. 1