Stanford's Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task
Classical coreference systems encode various syntactic, discourse, and semantic phenomena explicitly, using heterogenous features computed from hand-crafted heuristics. In contrast , we present a state-of-the-art coreference system that captures such phenomena implicitly , with a small number of homogeneous feature templates examining shallow properties of mentions. Surprisingly, our features are actually more effective than the corresponding hand-engineered ones at modeling these key linguistic phenomena, allowing us to win " easy victories " without crafted heuris-tics. These features are successful on syntax and discourse; however, they do not model semantic compatibility well, nor do we see gains from experiments with shallow semantic features from the literature, suggesting that this approach to semantics is an " uphill battle. " Nonetheless, our final system 1 outper-forms the Stanford system (Lee et al. (2011), the winner of the CoNLL 2011 shared task) by 3.5% absolute on the CoNLL metric and outperforms the IMS system (Björkelund and Farkas (2012), the best publicly available En-glish coreference system) by 1.9% absolute.