Ensemble-Based Coreference Resolution

Employing different We investigate new methods for creating and applying coreference models to create ensembles bears resemblance ensembles for coreference resolution. While to Pang and Fan's [2009] approach, where an ensemble of existing ensembles for coreference resolution are pairwise models is applied to Chinese coreference resolution, typically created using different learning algorithms, but contrasts with the vast majority of existing approaches, clustering algorithms or training sets, we where an ensemble of coreference systems is typically created harness recent advances in coreference modeling by employing different learning algorithms [Munson et and propose to create our ensemble from a variety al., 2005] or clustering algorithms [Ng, 2005], or perturbing of supervised coreference models. However, the training set using meta-learning techniques such as the presence of pairwise and non-pairwise coreference bagging and boosting [Ng and Cardie, 2003; Kouchnir, 2004; models in our ensemble presents a challenge Vemulapalli et al., 2009].