Rei Odaira, Jose G. Castanos, et al.
IISWC 2013
Classifier combination techniques have been applied to a number of natural language processing problems. This paper explores the use of bagging and boosting as combination approaches for coreference resolution. To the best of our knowledge, this is the first effort that examines and evaluates the applicability of such techniques to coreference resolution. In particular, we (1) outline a scheme for adapting traditional bagging and boosting techniques to address issues, like entity alignment, that are specific to coreference resoluti on, (2) provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of multiple classifiers, without any additional features or training data, and (3) implement and evaluate combination techniques at the mention, entity and document level.
Rei Odaira, Jose G. Castanos, et al.
IISWC 2013
Liya Fan, Fa Zhang, et al.
JPDC
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
Ryan Johnson, Ippokratis Pandis
CIDR 2013