Specialized models and ranking for coreference resolution

Authors:
Pascal Denis;Jason Baldridge
Affiliations:
INRIA Rocquencourt, Le Chesnay, France;University of Texas at Austin, Austin, TX
Venue:
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Year:
2008

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Abstract

This paper investigates two strategies for improving coreference resolution: (1) training separate models that specialize in particular 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 significant performance improvements. Specifically, 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).