Traditional learning-based coreference resolvers operate by training the
mention-pair model for determining whether two mentions are coreferent or not.
Though conceptually simple and easy to understand, the mention-pair model is
linguistically rather unappealing and lags far behind the heuristic-based
coreference models proposed in the pre-statistical NLP era in terms of
sophistication. Two independent lines of recent research have attempted to
improve the mention-pair model, one by acquiring the mention-ranking model to
rank preceding mentions for a given anaphor, and the other by training the
entity-mention model to determine whether a preceding cluster is coreferent
with a given mention. We propose a cluster-ranking approach to coreference
resolution, which combines the strengths of the mention-ranking model and the
entity-mention model, and is therefore theoretically more appealing than both
of these models. In addition, we seek to improve cluster rankers via two
extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity
by jointly modeling anaphoricity determination and coreference resolution.
Experimental results on the ACE data sets demonstrate the superior performance
of cluster rankers to competing approaches as well as the effectiveness of our
two extensions.

提出了一种基于聚类排名的共指解析方法，该方法结合了提及排名和实体 - 提及模型的优点，并通过两个扩展来改进聚类排名者的性能： (1) 词汇化和 (2) 通过联合建模代表性确定和共指解析来结合关于先行词类型的知识。实验结果表明，与竞争方法相比，此方法具有卓越的性能，并且我们的两个扩展措施是有效的。