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Specialized Models and Ranking for Coreference Resolution

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, B^3, and CEAF).
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Contributor : Pascal Denis Connect in order to contact the contributor
Submitted on : Thursday, September 2, 2010 - 9:09:56 AM
Last modification on : Friday, November 18, 2022 - 9:25:58 AM


  • HAL Id : inria-00514368, version 1



Pascal Denis, Jason Baldridge. Specialized Models and Ranking for Coreference Resolution. Empirical Methods on Natural Language Processing, 2008, Honolulu, Hawaï, United States. pp.660-669. ⟨inria-00514368⟩



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