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        <title>EDeR: Towards Understanding Dependency Relations Between Events</title>
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    <abstract>Relation extraction is a crucial task in natural language processing (NLP) and information retrieval (IR). Previous work on event relation extraction mainly focuses on hierarchical, temporal and causal relations. Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR). The annotation is done on a sample of documents from the OntoNotes dataset, which has the additional benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for EDeR’s event dependency relation prediction. We show that recognizing such event dependency relations can further benefit critical NLP tasks, including semantic role labelling and co-reference resolution.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.926</identifier>
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%0 Conference Proceedings
%T EDeR: Towards Understanding Dependency Relations Between Events
%A Li, Ruiqi
%A Haslum, Patrik
%A Cui, Leyang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-eder
%X Relation extraction is a crucial task in natural language processing (NLP) and information retrieval (IR). Previous work on event relation extraction mainly focuses on hierarchical, temporal and causal relations. Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR). The annotation is done on a sample of documents from the OntoNotes dataset, which has the additional benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for EDeR’s event dependency relation prediction. We show that recognizing such event dependency relations can further benefit critical NLP tasks, including semantic role labelling and co-reference resolution.
%R 10.18653/v1/2023.emnlp-main.926
%U https://aclanthology.org/2023.emnlp-main.926
%U https://doi.org/10.18653/v1/2023.emnlp-main.926
%P 14969-14983

Markdown (Informal)

[EDeR: Towards Understanding Dependency Relations Between Events](https://aclanthology.org/2023.emnlp-main.926) (Li et al., EMNLP 2023)

• EDeR: Towards Understanding Dependency Relations Between Events (Li et al., EMNLP 2023)

ACL

• Ruiqi Li, Patrik Haslum, and Leyang Cui. 2023. EDeR: Towards Understanding Dependency Relations Between Events. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14969–14983, Singapore. Association for Computational Linguistics.