Syntactic Parsing for Ranking-Based Coreference Resolution

Recent research efforts have led to the development of a state-of-the-art supervised coreference model, the cluster-ranking model. However, it is not clear whether the features that have been shown to be useful when employed in traditional coreference models will fare similarly when used in combination with this new model. Rather than merely re-evaluate them using the cluster-ranking model, we examine two interesting types of features derived from syntactic parses, tree-based features and path-based features, and discuss the challenges involved in employing them in the cluster-ranking model. Results on a set of Switchboard dialogues show their effectiveness in improving the cluster-ranking model: using them to augment a baseline coreference feature set yields a 8.6–11.7% reduction in relative error.