In this section, we propose a method for resolving this conflict: we introduce an error-driven rule pruning algorithm that considers rules induced by the coreference classifier and discards those that cause the ruleset to perform poorly with respect to the global, clustering-level coreference scoring function.
all (positive and negative) training instances formed with NP¨£ as the anaphor are no longer needed and are subsequently removed from the training data.