Our goal in this paper is to improve the robustness of the standard approach by addressing the above weaknesses.
we propose the following procedure for coreference resolution: given a set of NPs to be clustered, (1) use pre-selected learningbased coreference systems to generate candidate partitions of the NPs, and then (2) apply an automatically acquired ranking model to rank these candidate hypotheses, selecting the best one to be the final partition.