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

Autor:	Binbin Shi, Rongli Fan, Lijuan Zhang, Jie Huang, Neal Xiong, Athanasios Vasilakos, Jian Wan, Lei Zhang
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	joint extraction talking-head attention Chinese medical texts RoBERTa health monitoring Chemical technology TP1-1185
Zdroj:	Sensors, Vol 23, Iss 10, p 4812 (2023)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s23104812
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5a6f32a11ebf415ea4a588a6f986b36e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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