We describe an approach to coreference resolution that relies on the intuition that easy decisions should be made early, while harder decisions should be left for later when more information is available. We are inspired by the recent success of the rule-based system of Raghunathan et al. (2010), which relies on the same intuition. Our system, however, automatically learns from training data what constitutes an easy decision. Thus, we can utilize more features, learn more precise weights, and adapt to any dataset for which training data is available. Experiments show that our system outperforms recent state-of-the-art coreference systems including Raghunathan et al.'s system as well as a competitive baseline that uses a pairwise classifier.
Easy-first strategy relies on the intuition that "easy decisions should be made early, while harder decisions should be left for later when more information is available REF ".
Easy-first Coreference Resolution
COLING
Computer Science