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        <title>Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures</title>
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    <name type="personal">
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            <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1045</identifier>
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%0 Conference Proceedings
%T Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
%A Choubey, Prafulla Kumar
%A Huang, Ruihong
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F choubey-huang-2018-improving
%X 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.
%R 10.18653/v1/P18-1045
%U https://aclanthology.org/P18-1045
%U https://doi.org/10.18653/v1/P18-1045
%P 485-495

Markdown (Informal)

[Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures](https://aclanthology.org/P18-1045) (Choubey & Huang, ACL 2018)

• Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures (Choubey & Huang, ACL 2018)

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.