ArticleBMC bioinformatics2022
Hierarchical shared transfer learning for biomedical named entity recognition.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.BMC bioinformatics · 2025Article
- RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- An ensemble novel architecture for Bangla Mathematical Entity Recognition (MER) using transformer based learning.Heliyon · 2024Article
- Article
- AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.Bioinformatics (Oxford, England) · 2023Article
- A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.Healthcare (Basel, Switzerland) · 2023Review
- A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition.BMC bioinformatics · 2023Article
- Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora.Computational biology and chemistry · 2023Article
- Biomedical named entity recognition based on fusion multi-features embedding.Technology and health care : official journal of the European Society for Engineering and Medicine · 2023Article
- BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework.BMC bioinformatics · 2022Article
- Biomedical named entity recognition with the combined feature attention and fully-shared multi-task learning.BMC bioinformatics · 2022Article
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6 authors.
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Abstract
backgroundBiomedical named entity recognition (BioNER) is a basic and important medical information extraction task to extract medical entities with special meaning from medical texts. In recent years, deep learning has become the main research direction of BioNER due to its excellent data-driven context coding ability. However, in BioNER task, deep learning has the problem of poor generalization and instability.
resultswe propose the hierarchical shared transfer learning, which combines multi-task learning and fine-tuning, and realizes the multi-level information fusion between the underlying entity features and the upper data features. We select 14 datasets containing 4 types of entities for training and evaluate the model. The experimental results showed that the F1-scores of the five gold standard datasets BC5CDR-chemical, BC5CDR-disease, BC2GM, BC4CHEMD, NCBI-disease and LINNAEUS were increased by 0.57, 0.90, 0.42, 0.77, 0.98 and - 2.16 compared to the single-task XLNet-CRF model. BC5CDR-chemical, BC5CDR-disease and BC4CHEMD achieved state-of-the-art results.The reasons why LINNAEUS's multi-task results are lower than single-task results are discussed at the dataset level.
conclusionCompared with using multi-task learning and fine-tuning alone, the model has more accurate recognition ability of medical entities, and has higher generalization and stability.
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