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 …

Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Recent advancements in large language models (LLMs) have led to the development of
highly potent models like OpenAI's ChatGPT. These models have exhibited exceptional …

Numerous important events happen everyday and everywhere but are reported in different
media sources with different narrative styles. How to detect whether real-world events have …

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant
biomedical knowledge in scientific literature to understand the disease mechanism and …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

Event detection (ED), which means identifying event trigger words and classifying event
types, is the first and most fundamental step for extracting event knowledge from plain text …

The growing demand for structured knowledge has led to great interest in relation extraction,
especially in cases with limited supervision. However, existing distance supervision …

Abstract The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured
four tracks. The first of these was the de-identification track focused on identifying protected …

One effective way to improve the state of the art is through competitions. Following the
success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics …