Using Bagging and Boosting Techniques for Improving Coreference Resolution

Classifier combination techniques have been applied to a number of natural language processing problems. This paper explores the use of bagging and boosting as combination approaches for coreference resolution. To the best of our knowledge, this is the first effort that examines and evaluates the applicability of such techniques to coreference resolution. In particular, we (1) outline a scheme for adapting traditional bagging and boosting techniques to address issues, like entity alignment, that are specific to coreference resolution, (2) provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of multiple classifiers, without any additional features or training data, and (3) implement and evaluate combination techniques at the mention, entity and document level.