Error Analysis for Learning-based Coreference Resolution

Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008)

Abstract

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.