Error Analysis for Learning-based Coreference Resolution
Olga Uryupina · 2008
State-of-the-art coreference resolution engines show similar performance figures (low sixties on the MUC-7 data).Our system with a rich linguistically motivated feature set yields significantly better performance values for a variety of machine learners, but still leaves substantial room for improvement.In this paper we address a relatively unexplored area of coreference resolution -we present a detailed error analysis in order to understand the issues raised by corpus-based approaches to coreference resolution.