A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.

Binbin Shi, Rongli Fan, Lijuan Zhang, Jie Huang, Neal Xiong, Athanasios Vasilakos, Jian Wan, Lei Zhang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Binbin ShiSchool of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Rongli FanSchool of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Lijuan ZhangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Jie HuangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Neal XiongDepartment of Computer Science, Mathematics Sul Ross State University, Alpine, TX 79830, USA.ORCID 0000-0002-0394-4635

Athanasios VasilakosCenter for AI Research, University of Agder, 4879 Grimstad, Norway.

Jian WanSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Lei ZhangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Funding

Zhejiang Province Key Research and Development Project 2021C03145

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceNatural Language ProcessingRecognition, PsychologyTechnologyChinese medical textshealth monitoringjoint extractionRoBERTatalking-head attention

Identifiers

PMID37430725

DOI10.3390/s23104812

PMCPMC10222436

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.