Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events

Prafulla Kumar Choubey, Ruihong Huang


Abstract
We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains. Among event mentions in the same chain, we distinguish within- and cross-document event coreference links by using two distinct pairwise classifiers, trained separately to capture differences in feature distributions of within- and cross-document event clusters. Our event coreference approach alternates between WD and CD clustering and combines arguments from both event clusters after every merge, continuing till no more merge can be made. And then it performs further merging between event chains that are both closely related to a set of other chains of events. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods in joint task of WD and CD event coreference resolution.
Anthology ID:
D17-1226
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2124–2133
Language:
URL:
https://www.aclweb.org/anthology/D17-1226
DOI:
10.18653/v1/D17-1226
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1226.pdf