372 citations · 1.8k across the 28 of their papers we have counts for
7 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…
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning
Robert Leaman, Rezarta Islamaj, Alexis Allot +3
A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of S…
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…
LitMC-BERT: transformer-based multi-label classification of biomedical literature with an application on COVID-19 literature curation
Qingyu Chen, Jingcheng Du, Alexis Allot +1
The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a lite…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis
Qiyuan An, Ruijiang Li, Lin Gu +5
Unsupervised domain adaptation (UDA) generally aligns the unlabeled target domain data to the distribution of the source domain to mitigate the distribution shift problem. The stan…