TitleApplication of cascade binary pointer tagging in joint entity and relation extraction of Chinese medical text
AuthorsChang, Hongyang
Zan, Hongying
Guan, Tongfeng
Zhang, Kunli
Sui, Zhifang
AffiliationZhengzhou Univ, Sch Comp & Artificial Intelligence, Zhengzhou, Peoples R China
Pengcheng Lab, Shenzhen, Peoples R China
Peking Univ, Key Lab Computat Linguist, Beijing, Peoples R China
Issue Date2022
PublisherMATHEMATICAL BIOSCIENCES AND ENGINEERING
AbstractExtracting relational triples from unstructured medical texts can provide a basis for the construction of large-scale medical knowledge graphs. The cascade binary pointer tagging network (CBPTN) shows excellent performance in the joint entity and relation extraction, so we try to explore its effectiveness in the joint entity and relation extraction of Chinese medical texts. In this paper, we propose two models based on the CBPTN: CBPTN with conditional layer normalization (Cas-CLN) and biaffine transformation-based CBPTN with multi-head selection (BTCAMS). Cas-CLN uses the CBPTN to decode the head entity and relation-tail entity successively and utilizes conditional layer normalization to enhance the connection between the two steps. BTCAMS detects all possible entities in a sentence by using the CBPTN and then determines the relation between each entity pair through biaffine transformation. We test the performance of the two models on two Chinese medical datasets: CMeIE and CEMRDS. The experimental results prove the effectiveness of the two models. Compared with the baseline CasREL, the F1 value of Cas-CLN and BTCAMS on the test data of CMeIE improved by 1.01 and 2.13%; on the test data of CEMRDS, the F1 value improved by 1.99 and 0.68%.
URIhttp://hdl.handle.net/20.500.11897/657550
ISSN1547-1063
DOI10.3934/mbe.2022498
IndexedEI
SCI(E)
Appears in Collections:计算语言学教育部重点实验室

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