In addition, our experiments indicate that the positive sample selection algorithm does not guarantee that hard instances can be entirely excluded.
we proposed an error-driven rule pruning algorithm that can effectively enhance the precision of the system by dis-carding rules that cause the ruleset to perform poorly with respect to the coreference scoring function.