Coreference Resolution with Reconcile

Despite the existence of several noun phrase coreference resolution data sets as well as several formal evaluations on the task, it remains frustratingly difficult to compare results across different coreference resolution systems. This is due to the high cost of implementing a complete end-to-end coreference resolution system, which often forces researchers to substitute available gold-standard information in lieu of implementing a module that would compute that information. Unfortunately, this leads to inconsistent and often unrealistic evaluation scenarios. With the aim to facilitate consistent and realistic experimental evaluations in coreference resolution, we present Reconcile, an infrastructure for the development of learning-based noun phrase (NP) coreference resolution systems. Reconcile is designed to facilitate the rapid creation of coreference resolution systems, easy implementation of new feature sets and approaches to coreference resolution, and empirical evaluation of coreference resolvers across a variety of benchmark data sets and standard scoring metrics. We describe Reconcile and present experimental results showing that Reconcile can be used to create a coreference resolver that achieves performance comparable to state-of-the-art systems on six benchmark data sets.