Paper: Specialized Models and Ranking for Coreference Resolution

ACL ID D08-1069
Title Specialized Models and Ranking for Coreference Resolution
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

This paper investigates two strategies for im- proving coreference resolution: (1) training separate models that specialize in particu- lar types of mentions (e.g., pronouns versus proper nouns) and (2) using a ranking loss function rather than a classification function. In addition to being conceptually simple, these modifications of the standard single-model, classification-based approach also deliver sig- nificant performance improvements. Specifi- cally, we show that on the ACE corpus both strategies produce f-score gains of more than 3% across the three coreference evaluation metrics (MUC, B3, and CEAF).