EDeR: A Dataset for Exploring Dependency Relations Between Events
This work addresses a novel type of relation extraction for NLP and IR researchers, but it is incremental as it builds on existing datasets and tasks.
The paper tackles the problem of extracting dependency relations between events, which had not been explored in NLP or IR research, by introducing the EDeR dataset and showing that recognizing this relation improves event extraction accuracy and downstream tasks like co-reference resolution.
Relation extraction is a central task in natural language processing (NLP) and information retrieval (IR) research. We argue that an important type of relation not explored in NLP or IR research to date is that of an event being an argument - required or optional - of another event. We introduce the human-annotated Event Dependency Relation dataset (EDeR) which provides this dependency relation. The annotation is done on a sample of documents from the OntoNotes dataset, which has the added benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for predicting the event dependency relation, the best of which achieves an accuracy of 82.61 for binary argument/non-argument classification. We show that recognizing this relation leads to more accurate event extraction (semantic role labelling) and can improve downstream tasks that depend on this, such as co-reference resolution. Furthermore, we demonstrate that predicting the three-way classification into the required argument, optional argument or non-argument is a more challenging task.