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Multi-task learning for Chinese clinical named entity recognition with external knowledge
BACKGROUND: Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale lab...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719412/ https://www.ncbi.nlm.nih.gov/pubmed/34972505 http://dx.doi.org/10.1186/s12911-021-01717-1 |
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author | Cheng, Ming Xiong, Shufeng Li, Fei Liang, Pan Gao, Jianbo |
author_facet | Cheng, Ming Xiong, Shufeng Li, Fei Liang, Pan Gao, Jianbo |
author_sort | Cheng, Ming |
collection | PubMed |
description | BACKGROUND: Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. METHODS: To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. RESULTS: In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. CONCLUSIONS: The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models. |
format | Online Article Text |
id | pubmed-8719412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87194122022-01-05 Multi-task learning for Chinese clinical named entity recognition with external knowledge Cheng, Ming Xiong, Shufeng Li, Fei Liang, Pan Gao, Jianbo BMC Med Inform Decis Mak Research BACKGROUND: Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities. METHODS: To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition. RESULTS: In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset. CONCLUSIONS: The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models. BioMed Central 2021-12-31 /pmc/articles/PMC8719412/ /pubmed/34972505 http://dx.doi.org/10.1186/s12911-021-01717-1 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Cheng, Ming Xiong, Shufeng Li, Fei Liang, Pan Gao, Jianbo Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title | Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title_full | Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title_fullStr | Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title_full_unstemmed | Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title_short | Multi-task learning for Chinese clinical named entity recognition with external knowledge |
title_sort | multi-task learning for chinese clinical named entity recognition with external knowledge |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719412/ https://www.ncbi.nlm.nih.gov/pubmed/34972505 http://dx.doi.org/10.1186/s12911-021-01717-1 |
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