A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring
Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4812 |
| _version_ | 1797598377697869824 |
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| author | Binbin Shi Rongli Fan Lijuan Zhang Jie Huang Neal Xiong Athanasios Vasilakos Jian Wan Lei Zhang |
| author_facet | Binbin Shi Rongli Fan Lijuan Zhang Jie Huang Neal Xiong Athanasios Vasilakos Jian Wan Lei Zhang |
| author_sort | Binbin Shi |
| collection | DOAJ |
| description | Natural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines. |
| first_indexed | 2024-03-11T03:20:19Z |
| format | Article |
| id | doaj.art-5a6f32a11ebf415ea4a588a6f986b36e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:19Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5a6f32a11ebf415ea4a588a6f986b36e2023-11-18T03:12:56ZengMDPI AGSensors1424-82202023-05-012310481210.3390/s23104812A Joint Extraction System Based on Conditional Layer Normalization for Health MonitoringBinbin Shi0Rongli Fan1Lijuan Zhang2Jie Huang3Neal Xiong4Athanasios Vasilakos5Jian Wan6Lei Zhang7School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaDepartment of Computer Science, Mathematics Sul Ross State University, Alpine, TX 79830, USACenter for AI Research, University of Agder, 4879 Grimstad, NorwaySchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaNatural language processing (NLP) technology has played a pivotal role in health monitoring as an important artificial intelligence method. As a key technology in NLP, relation triplet extraction is closely related to the performance of health monitoring. In this paper, a novel model is proposed for joint extraction of entities and relations, combining conditional layer normalization with the talking-head attention mechanism to strengthen the interaction between entity recognition and relation extraction. In addition, the proposed model utilizes position information to enhance the extraction accuracy of overlapping triplets. Experiments on the Baidu2019 and CHIP2020 datasets demonstrate that the proposed model can effectively extract overlapping triplets, which leads to significant performance improvements compared with baselines.https://www.mdpi.com/1424-8220/23/10/4812joint extractiontalking-head attentionChinese medical textsRoBERTahealth monitoring |
| spellingShingle | Binbin Shi Rongli Fan Lijuan Zhang Jie Huang Neal Xiong Athanasios Vasilakos Jian Wan Lei Zhang A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring Sensors joint extraction talking-head attention Chinese medical texts RoBERTa health monitoring |
| title | A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring |
| title_full | A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring |
| title_fullStr | A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring |
| title_full_unstemmed | A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring |
| title_short | A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring |
| title_sort | joint extraction system based on conditional layer normalization for health monitoring |
| topic | joint extraction talking-head attention Chinese medical texts RoBERTa health monitoring |
| url | https://www.mdpi.com/1424-8220/23/10/4812 |
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