Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures

Prafulla Kumar Choubey, Ruihong Huang


Abstract
This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. We explicitly model correlations between the main event chains of a document with topic transition sentences, inter-coreference chain correlations, event mention distributional characteristics and sub-event structure, and use them with scores obtained from a local coreference relation classifier for jointly resolving multiple event chains in a document. Our experiments across KBP 2016 and 2017 datasets suggest that each of the structures contribute to improving event coreference resolution performance.
Anthology ID:
P18-1045
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
485–495
Language:
URL:
https://aclanthology.org/P18-1045
DOI:
10.18653/v1/P18-1045
Bibkey:
Cite (ACL):
Prafulla Kumar Choubey and Ruihong Huang. 2018. Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 485–495, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures (Choubey & Huang, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1045.pdf
Poster:
 P18-1045.Poster.pdf
Data
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