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• W2799167615 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." @default.
• W2799167615 created "2018-05-07" @default.
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• W2799167615 date "2018-01-01" @default.
• W2799167615 modified "2023-10-11" @default.
• W2799167615 title "Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures" @default.
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• W2799167615 doi "https://doi.org/10.18653/v1/p18-1045" @default.
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