ArticlePloS one2021
A pre-training and self-training approach for biomedical named entity recognition.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 48 citations in OpenAlex.
- Optimising Generalisable Deep Learning Models for CT Coronary Segmentation: A Multifactorial Evaluation.Journal of imaging informatics in medicine · 2026Article
- I-NOWJ: adaptive context selection and knowledge-guided learning for substance use named entity recognition.Database : the journal of biological databases and curation · 2026Article
- Article
- Leveraging Language Models for Automated Label Generation in Traumatic Brain Injury Radiology Reports.Research square · 2025Article
- MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.Interdisciplinary sciences, computational life sciences · 2024Article
- Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation.Journal of medical Internet research · 2024Article
- The role of large language models in medical image processing: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach.BMC medical informatics and decision making · 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
- Large-scale application of named entity recognition to biomedicine and epidemiology.PLOS digital health · 2022Article
- A review on Natural Language Processing Models for COVID-19 research.Healthcare analytics (New York, N.Y.) · 2022Review
- PICO entity extraction for preclinical animal literature.Systematic reviews · 2022Article
- Accelerated variant curation from scientific literature using biomedical text mining.microPublication biology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.