Chinese medical named entity recognition model based on local enhancement
In the medical field, the recognition of medical entities is often influenced by their adjacent context, the current named entity recognition methods typically rely on BiLSTM to capture the global dependency relationships within text, lacking modeling of local dependencies between characters. To res...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024117/ |
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| _version_ | 1841539206617235456 |
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| author | CHEN Jing XING Kexuan MENG Weilun GUO Jingfeng FENG Jianzhou |
| author_facet | CHEN Jing XING Kexuan MENG Weilun GUO Jingfeng FENG Jianzhou |
| author_sort | CHEN Jing |
| collection | DOAJ |
| description | In the medical field, the recognition of medical entities is often influenced by their adjacent context, the current named entity recognition methods typically rely on BiLSTM to capture the global dependency relationships within text, lacking modeling of local dependencies between characters. To resolve this problem, a Chinese medical named entity recognition model LENER based on local enhancement was proposed. Firstly, the representation of characters was enriched by LENER utilizing multi-source information, including phonetic, graphic and semantic features. Secondly, relative position encoding was combined to perform local attention calculations on sequence segments divided by sliding windows, and local information was fused with global information obtained from BiLSTM through nonlinear computation. Finally, the recognized entity heads and tails were combined by LENER to extract the entities. The experimental results show that the LENER model has excellent entity recognition capabilities, and the <italic>F</italic><sub>1</sub> value is improved by 0.5% to 2% compared with other models. |
| format | Article |
| id | doaj-art-7ac8fad1acd440dcb3f41c5a6f42b435 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-7ac8fad1acd440dcb3f41c5a6f42b4352025-01-14T07:24:42ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-07-014517118367384751Chinese medical named entity recognition model based on local enhancementCHEN JingXING KexuanMENG WeilunGUO JingfengFENG JianzhouIn the medical field, the recognition of medical entities is often influenced by their adjacent context, the current named entity recognition methods typically rely on BiLSTM to capture the global dependency relationships within text, lacking modeling of local dependencies between characters. To resolve this problem, a Chinese medical named entity recognition model LENER based on local enhancement was proposed. Firstly, the representation of characters was enriched by LENER utilizing multi-source information, including phonetic, graphic and semantic features. Secondly, relative position encoding was combined to perform local attention calculations on sequence segments divided by sliding windows, and local information was fused with global information obtained from BiLSTM through nonlinear computation. Finally, the recognized entity heads and tails were combined by LENER to extract the entities. The experimental results show that the LENER model has excellent entity recognition capabilities, and the <italic>F</italic><sub>1</sub> value is improved by 0.5% to 2% compared with other models.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024117/Chinese named entity recognitioncontextual environmentattention mechanismmulti-source informationsliding window |
| spellingShingle | CHEN Jing XING Kexuan MENG Weilun GUO Jingfeng FENG Jianzhou Chinese medical named entity recognition model based on local enhancement Tongxin xuebao Chinese named entity recognition contextual environment attention mechanism multi-source information sliding window |
| title | Chinese medical named entity recognition model based on local enhancement |
| title_full | Chinese medical named entity recognition model based on local enhancement |
| title_fullStr | Chinese medical named entity recognition model based on local enhancement |
| title_full_unstemmed | Chinese medical named entity recognition model based on local enhancement |
| title_short | Chinese medical named entity recognition model based on local enhancement |
| title_sort | chinese medical named entity recognition model based on local enhancement |
| topic | Chinese named entity recognition contextual environment attention mechanism multi-source information sliding window |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024117/ |
| work_keys_str_mv | AT chenjing chinesemedicalnamedentityrecognitionmodelbasedonlocalenhancement AT xingkexuan chinesemedicalnamedentityrecognitionmodelbasedonlocalenhancement AT mengweilun chinesemedicalnamedentityrecognitionmodelbasedonlocalenhancement AT guojingfeng chinesemedicalnamedentityrecognitionmodelbasedonlocalenhancement AT fengjianzhou chinesemedicalnamedentityrecognitionmodelbasedonlocalenhancement |