2013 DeterministicCoreferenceResolut

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Notes

We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of deterministic, rule-based systems. Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model's cluster output. The two stages of our sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall. Further, our approach makes use of global information through an entity-centric model that encourages the sharing of features across all mentions that point to the same real-world entity. Despite its simplicity, our approach gives state-of-the-art performance on several corpora and genres, and has also been incorporated into hybrid state-of-the-art coreference systems for Chinese and Arabic. Our system thus offers a new paradigm for combining knowledge in rule-based systems that has implications throughout computational linguistics.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 DeterministicCoreferenceResolut	Mihai Surdeanu Daniel Jurafsky Heeyoung Lee Angel Chang Yves Peirsman Nathanael Chambers			Deterministic Coreference Resolution based on Entity-centric, Precision-ranked Rules				10.1162/COLI_a_00152		2013