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 …

Event extraction has gained considerable interest due to its wide-ranging applications.
However, recent studies draw attention to evaluation issues, suggesting that reported scores …

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 …

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 …

We consider event extraction in a generative manner with template-based conditional
generation. Although there is a rising trend of casting the task of event extraction as a …

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 …

We compare various forms of prompts to represent event types and develop a unified
framework to incorporate the event type specific prompts for supervised, few-shot, and zero …

Extracting entities, events, event arguments, and relations (ie, task instances) from text
represents four main challenging tasks in information extraction (IE), which have been …

Data scarcity has been the main factor that hinders the progress of event extraction. To
overcome this issue, we propose a Self-Training with Feedback (STF) framework that …

Universal Information Extraction (UIE) is an area of interest due to the challenges posed by
varying targets, heterogeneous structures, and demand-specific schemas. However …