ChatGPT has drawn considerable attention from both the general public and domain experts
with its remarkable text generation capabilities. This has subsequently led to the emergence …

Named Entity Recognition seeks to extract substrings within a text that name real-world
objects and to determine their type (for example, whether they refer to persons or …

Abstract Motivation Large Language Models (LLMs) have the potential to revolutionize the
field of Natural Language Processing, excelling not only in text generation and reasoning …

Abstract PubTator 3.0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical
literature resource using state-of-the-art AI techniques to offer semantic and relation …

Abstract The BioRED track at BioCreative VIII calls for a community effort to identify,
semantically categorize, and highlight the novelty factor of the relationships between …

Motivation With the exponential growth of the life sciences literature, biomedical text mining
(BTM) has become an essential technology for accelerating the extraction of insights from …

The identification of medical concepts from clinical narratives has a large interest in the
biomedical scientific community due to its importance in treatment improvements or drug …

Expert curation is essential to capture knowledge of enzyme functions from the scientific
literature in FAIR open knowledgebases but cannot keep pace with the rate of new …

Objective Training a neural network-based biomedical named entity recognition (BioNER)
model usually requires extensive and costly human annotations. While several studies have …

This paper presents our approach for the 2024 ChemoTimelines shared task. Specifically,
we explored using Large Language Models (LLMs) for temporal relation extraction. We …