ArticleBMC bioinformatics2019
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Attention-based interactive multi-level feature fusion for named entity recognition.Scientific reports · 2025Article
- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
- Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.JMIR medical informatics · 2024Article
- Application of machine reading comprehension techniques for named entity recognition in materials science.Journal of cheminformatics · 2024Article
- Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.Journal of proteome research · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- GPDminer: a tool for extracting named entities and analyzing relations in biological literature.BMC bioinformatics · 2024Article
- Dictionary-based matching graph network for biomedical named entity recognition.Scientific reports · 2023Article
- Constructing a disease database and using natural language processing to capture and standardize free text clinical information.Scientific reports · 2023Article
- Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies.JCO clinical cancer informatics · 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
- Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.BMC medical informatics and decision making · 2023Article
- A comparative study of pretrained language models for long clinical text.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.BMC digital health · 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
- Review
- Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.BMC medical research methodology · 2022Article
- Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.Viruses · 2022Article
- Extract antibody and antigen names from biomedical literature.BMC bioinformatics · 2022Article
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Authors and funding
4 authors.
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Abstract
backgroundFinding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition.
resultsTo address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score.
conclusionsWe demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.
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