Error Analysis for Learning-based Coreference Resolution

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

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Olga Uryupina
• Keywords: Coreference Resolution, Education, LREC 2008, Similar Performance Figures, State-of-the-art Coreference Resolution