A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition
The typical pretrained model’s feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new m...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/8969144 |
| _version_ | 1797816266887528448 |
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| author | Tingzhong Wang Yongxin Zhang Yifan Zhang Hao Lu Bo Yu Shoubo Peng Youzhong Ma Deguang Li |
| author_facet | Tingzhong Wang Yongxin Zhang Yifan Zhang Hao Lu Bo Yu Shoubo Peng Youzhong Ma Deguang Li |
| author_sort | Tingzhong Wang |
| collection | DOAJ |
| description | The typical pretrained model’s feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new model that combines the BERT pretraining model and the BilSTM-CRF model. First, word embedding with semantic information is obtained by pretraining the corpus input to the BERT model. Then, the BiLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding. Then, CRF is used to modify the results of BiLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models. |
| first_indexed | 2024-03-13T08:36:13Z |
| format | Article |
| id | doaj.art-92197bce92d34a168609330ebd15cd18 |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-03-13T08:36:13Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-92197bce92d34a168609330ebd15cd182023-05-31T00:00:03ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/8969144A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity RecognitionTingzhong Wang0Yongxin Zhang1Yifan Zhang2Hao Lu3Bo Yu4Shoubo Peng5Youzhong Ma6Deguang Li7School of Information TechnologySchool of Information TechnologySchool of Information TechnologySchool of Information TechnologySchool of Information TechnologyFaculty of Electrical Engineering and Computer ScienceSchool of Information TechnologySchool of Information TechnologyThe typical pretrained model’s feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new model that combines the BERT pretraining model and the BilSTM-CRF model. First, word embedding with semantic information is obtained by pretraining the corpus input to the BERT model. Then, the BiLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding. Then, CRF is used to modify the results of BiLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models.http://dx.doi.org/10.1155/2023/8969144 |
| spellingShingle | Tingzhong Wang Yongxin Zhang Yifan Zhang Hao Lu Bo Yu Shoubo Peng Youzhong Ma Deguang Li A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition Journal of Electrical and Computer Engineering |
| title | A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition |
| title_full | A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition |
| title_fullStr | A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition |
| title_full_unstemmed | A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition |
| title_short | A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition |
| title_sort | hybrid model based on deep convolutional network for medical named entity recognition |
| url | http://dx.doi.org/10.1155/2023/8969144 |
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