Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Compared to the general news domain, information extraction (IE) from biomedical text
requires much broader domain knowledge. However, many previous IE methods do not …

Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …

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 …

The primary goal of drug safety researchers and regulators is to promptly identify adverse
drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately …

Every day, enormous amounts of biomedical texts discussing various biomedical topics are
produced. Revealing strong semantic connections hidden in those unstructured data is …

Abstract Biomedical Information Extraction from scientific literature presents two unique and
nontrivial challenges. First, compared with general natural language texts, sentences from …

Contemporary news reporting increasingly features multimedia content, motivating research
on multimedia event extraction. However, the task lacks annotated multimodal training data …