A Ranking Approach to Pronoun Resolution

Pascal Denis, Jason Baldridge

We propose a supervised maximum entropy ranking approach to pronoun resolution as an alternative to commonly used classification-based approaches. Classification approaches consider only one or two candidate antecedents for a pronoun at a time, whereas ranking allows all candidates to be evaluated together. We argue that this provides a more natural fit for the task than classification and show that it delivers significant performance improvements on the ACE datasets. In particular, our ranker obtains an error reduction of 9.7% over the best classification approach, the twin-candidate model. Furthermore, we show that the ranker offers some computational advantage over the twin-candidate classifier, since it easily allows the inclusion of more candidate antecedents during training. This approach leads to a further error reduction of 5.4% (a total reduction of 14.6% over the twin-candidate model).