BERN2: an advanced neural biomedical named entity recognition and normalization tool
arXiv:2201.02080 · doi:10.1093/bioinformatics/btac598
Abstract
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.
Published in Bioinformatics 2022. Web service available at http://bern2.korea.ac.kr. Code available at https://github.com/dmis-lab/BERN2
Cited by in corpus (8)
- AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
- Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT
- PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science
- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools
- A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
- Augmenting Biomedical Named Entity Recognition with General-domain Resources
- BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
- Taec: a Manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature