@inproceedings{li-etal-2023-eder,
    title = "{ED}e{R}: Towards Understanding Dependency Relations Between Events",
    author = "Li, Ruiqi  and
      Haslum, Patrik  and
      Cui, Leyang",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-main.926/",
    doi = "10.18653/v1/2023.emnlp-main.926",
    pages = "14969--14983",
    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."
}