Deterministic coreference resolution based on entity-centric, precision-ranked rules

  • Authors:

  • Heeyoung Lee;Angel Chang;Yves Peirsman;Nathanael Chambers;Mihai Surdeanu;Dan Jurafsky

  • Affiliations:

  • Stanford University;Stanford University;University of Leuven;United States Naval Academy;University of Arizona;Stanford University

  • Venue:
  • Computational Linguistics
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.