Ensemble-Based Coreference Resolution

Altaf Rahman and Vincent Ng.
Proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp. 1884-1889, 2011.

Click here for the PostScript or PDF version. The talk slides are available here.


We investigate new methods for creating and applying ensembles for coreference resolution. While existing ensembles for coreference resolution are typically created using different learning algorithms, clustering algorithms or training sets, we harness recent advances in coreference modeling and propose to create our ensemble from a variety of supervised coreference models. However, the presence of pairwise and non-pairwise coreference models in our ensemble presents a challenge to its application: it is not immediately clear how to combine the coreference decisions made by these models. We investigate different methods for applying a model-heterogeneous ensemble for coreference resolution. Empirical results on the ACE data sets demonstrate the promise of ensemble approaches: all ensemble-based systems significantly outperform the best member of the ensemble.

Train-test split

Here are the lists of names of the files from the English portion of the ACE 2005 Multilingual Training Corpus we used for training and testing.

BibTeX entry

  author = {Altaf Rahman and Vincent Ng},
  title = {Ensemble-Based Coreference Resolution},
  booktitle = {Proceedings of the 22nd International Conference on Artificial Intelligence},
  pages = {1884--1889},
  year = 2011