Ensemble-Based Coreference Resolution
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1884-1888.
Abstract
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.
@inproceedings{ijcai2011p315,
title = {Ensemble-Based Coreference Resolution},
author = {Rahman, Altaf and Ng, Vincent},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1884--1888},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-315},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-315},
}
TY - CPAPER TI - Ensemble-Based Coreference Resolution AU - Rahman, Altaf AU - Ng, Vincent T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1884 EP - 1888 DO - 10.5591/978-1-57735-516-8/IJCAI11-315 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-315 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: