Paper: Improving pairwise coreference models through feature space hierarchy learning

ACL ID P13-1049
Title Improving pairwise coreference models through feature space hierarchy learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

This paper proposes a new method for significantly improving the performance of pairwise coreference models. Given a set of indicators, our method learns how to best separate types of mention pairs into equivalence classes for which we con- struct distinct classification models. In ef- fect, our approach finds an optimal fea- ture space (derived from a base feature set and indicator set) for discriminating coref- erential mention pairs. Although our ap- proach explores a very large space of pos- sible feature spaces, it remains tractable by exploiting the structure of the hierar- chies built from the indicators. Our exper- iments on the CoNLL-2012 Shared Task English datasets (gold mentions) indicate that our method is robust relative to dif- ferent clustering strategies and evaluation met...