Evaluating Hybrid Versus Data-Driven Coreference Resolution

In this paper, we present a systematic evaluation of a hybrid approach of combined rule-based filtering and machine learning to Dutch coreference resolution. Through the application of a selection of linguistically-motivated negative and positive filters, which we apply in isolation and combined, we study the effect of these filters on precision and recall using two different learning techniques: memory-based learning and maximum entropy modeling. Our results show that by using the hybrid approach, we can reduce up to 92 % of the training material without performance loss. We also show that the filters improve the overall precision of the classifiers leading to higher F-scores on the test set.