Recent work on Event Extraction has reframed the task as Question Answering (QA), with
promising results. The advantage of this approach is that it addresses the error propagation …

The EAE task extracts a structured event record from an event text. Most existing approaches
train the EAE model on each dataset independently and ignore the overlap knowledge …

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 present MatSci-NLP, a natural language benchmark for evaluating the performance of
natural language processing (NLP) models on materials science text. We construct the …

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 one of the fundamental tasks in Information Extraction (IE) that aims
to recognize event mentions and their arguments (ie, participants) from text. Due to its …

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 …

In-context learning (ICL) has become the default method for using large language models
(LLMs), making the exploration of its limitations and understanding the underlying causes …