EDeR: Towards Understanding Dependency Relations Between Events
Submission Type: Regular Long Paper
Submission Track: Resources and Evaluation
Keywords: dataset, event dependency relation
TL;DR: The paper proposes a dataset for exploring and understanding the dependency relations between events.
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.
Submission Number: 1716
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