Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Social media sites have become a popular platform for individuals to seek and share health
information. Despite the progress in natural language processing for social media mining, a …

Biomedical events represent complex, graphical, and semantically rich interactions
expressed in the scientific literature. Almost all contributions in the event realm orbit around …

Medical decisions directly impact individuals' health and well-being. Extracting decision
spans from clinical notes plays a crucial role in understanding medical decision-making …

Clinical diagnosis prediction models, when provided with a patient's medical history, aim to
detect potential diseases early, facilitating timely intervention and improving prognostic …

Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but
encounter distinct challenges in biomedicine due to medical language complexities and …

Information extraction tasks such as event extraction require an in-depth understanding of
the output structure and sub-task dependencies. They heavily rely on task-specific training …

Radiology reports serve as a fundamental component within electronic medical records.
Converting unstructured free-text reports into structured formats holds paramount …

Generative models have been widely used in event extraction. However, the interpretability
of event extraction has not been fully investigated. In this paper, we propose an E vent E …

Event argument extraction (EAE) aims to identify the spans and roles of arguments for the
given event type. Deep learning-based EAE methods, especially generation-based …