ArticlePloS one2019
Using distant supervision to augment manually annotated data for relation extraction.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction.BMC bioinformatics · 2022Article
- Identification of asthma control factor in clinical notes using a hybrid deep learning model.BMC medical informatics and decision making · 2021Article
- UMLS-based data augmentation for natural language processing of clinical research literature.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Deep Learning Identification of Asthma Inhaler Techniques in Clinical Notes.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Significant progress has been made in applying deep learning on natural language processing tasks recently. However, deep learning models typically require a large amount of annotated training data while often only small labeled datasets are available for many natural language processing tasks in biomedical literature. Building large-size datasets for deep learning is expensive since it involves considerable human effort and usually requires domain expertise in specialized fields. In this work, we consider augmenting manually annotated data with large amounts of data using distant supervision. However, data obtained by distant supervision is often noisy, we first apply some heuristics to remove some of the incorrect annotations. Then using methods inspired from transfer learning, we show that the resulting models outperform models trained on the original manually annotated sets.
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Registered trials
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