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 …

Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …

Automated relation extraction (RE) from biomedical literature is critical for many downstream
text mining applications in both research and real-world settings. However, most existing …

We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text
annotation supported by Natural Language Processing (NLP) technology. BRAT has been …

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 …

The paper presents the design and implementation of the BioNLP'09 Shared Task, and
reports the final results with analysis. The shared task consists of three sub-tasks, each of …

Abstract Background Named Entity Recognition (NER) is a key task in biomedical text
mining. Accurate NER systems require task-specific, manually-annotated datasets, which …

Large language models (LLMs) exhibited powerful capability in various natural language
processing tasks. This work focuses on exploring LLM performance on zero-shot information …