| Title | Hierarchical shared transfer learning for biomedical named entity recognition |
| Authors | Chai, Zhaoying Jin, Han Shi, Shenghui Zhan, Siyan Zhuo, Lin Yang, Yu |
| Affiliation | Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China Peking Univ, Sch Publ Hlth, Beijing, Peoples R China Peking Univ Third Hosp, Res Ctr Clin Epidemiol, Beijing, Peoples R China Peking Univ, Natl Inst Hlth Data Sci, Beijing, Peoples R China |
| Issue Date | 4-Jan-2022 |
| Publisher | BMC BIOINFORMATICS |
| Abstract | Background Biomedical 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. Results we 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. Conclusion Compared 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. |
| URI | http://hdl.handle.net/20.500.11897/634560 |
| ISSN | 1471-2105 |
| DOI | 10.1186/s12859-021-04551-4 |
| Indexed | EI SCI(E) SSCI |
| Appears in Collections: | 公共卫生学院 第三医院 |
