164 citations · 418 across the 20 of their papers we have counts for
5 papers · 1 filter
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Ling Luo, Chih-Hsuan Wei, Po-Ting Lai +3
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mi…
LitCovid in 2022: an information resource for the COVID-19 literature
Qingyu Chen, Alexis Allot, Robert Leaman +5
LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), first launched in February 2020, is a first-of-its-kind literature hub for tracking up-to-date published research on…
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
Ling Luo, Chih-Hsuan Wei, Po-Ting Lai +3
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene menti…
tmVar 3.0: an improved variant concept recognition and normalization tool
Chih-Hsuan Wei, Alexis Allot, Kevin Riehle +2
Previous studies have shown that automated text-mining tools are becoming increasingly important for successfully unlocking variant information in scientific literature at large sc…
BioRED: A Rich Biomedical Relation Extraction Dataset
Ling Luo, Po-Ting Lai, Chih-Hsuan Wei +2
Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most exist…