An incremental model for coreference resolution with restrictive antecedent accessibility

Authors:
Manfred Klenner;Don Tuggener
Affiliations:
University of Zurich;University of Zurich
Venue:
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
Year:
2011

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Abstract

We introduce an incremental model for coreference resolution that competed in the CoNLL 2011 shared task (open regular). We decided to participate with our baseline model, since it worked well with two other datasets. The benefits of an incremental over a mention-pair architecture are: a drastic reduction of the number of candidate pairs, a means to overcome the problem of underspecified items in pairwise classification and the natural integration of global constraints such as transitivity. We do not apply machine learning, instead the system uses an empirically derived salience measure based on the dependency labels of the true mentions. Our experiments seem to indicate that such a system already is on par with machine learning approaches.