The challenge of information extraction (IE) lies in the diversity of label schemas and the
heterogeneity of structures. Traditional methods require task-specific model design and rely …

Recent mainstream event argument extraction methods process each event in isolation,
resulting in inefficient inference and ignoring the correlations among multiple events. To …

Event Extraction (EE) is an essential task in natural language processing that aims to mine
events occurring in event mentions represent events using event records, which usually …

Event extraction for the clinical domain is an under-explored research area. The lack of
training data along with the high volume of domain-specific terminologies with vague entity …

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Lifelong event detection aims to incrementally update a model with new event types and
data while retaining the capability on previously learned old types. One critical challenge is …

Most existing methods regard event extraction as the classification task. They not only
heavily rely on named entity recognition, causing error propagation, but are also inefficient …

Recent works in Event Argument Extraction (EAE) have focused on improving model
generalizability to cater to new events and domains. However, standard benchmarking …

Determining the role of event arguments is a crucial subtask of event extraction. Most
previous supervised models leverage costly annotations, which is not practical for open …

Extracting temporal relations (eg, before, after, and simultaneous) among events is crucial to
natural language understanding. One of the key challenges of this problem is that when the …