372 citations · 624 across the 5 of their papers we have counts for
6 papers · 1 filter
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
Po-Ting Lai, Chih-Hsuan Wei, Ling Luo +2
Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomed…
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…
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…
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…
BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer
Po-Ting Lai, Zhiyong Lu
A biomedical relation statement is commonly expressed in multiple sentences and consists of many concepts, including gene, disease, chemical, and mutation. To automatically extract…
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
Ling Luo, Shankai Yan, Po-Ting Lai +7
Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dic…