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Indexed by:Journal Papers

First Author:Luo, Ling

Correspondence Author:Yang, ZH (reprint author), Dalian Univ Technol, Coll Comp Sci & Technol, Dalian 116024, Peoples R China.; Wang, L (reprint author), Beijing Inst Hlth Adm & Med Informat, Beijing 100850, Peoples R China.

Co-author:Yang, Zhihao,Cao, Mingyu,Wang, Lei,Zhang, Yin,Lin, Hongfei

Date of Publication:2020-03-01

Journal:JOURNAL OF BIOMEDICAL INFORMATICS

Included Journals:PubMed、EI、SCIE

Volume:103

Page Number:103384

ISSN No.:1532-0464

Key Words:Joint learning; Biomedical entity relation extraction; Att-BiLSTM-CRF; Biomedical ELMo

Abstract:Recently joint modeling methods of entity and relation exhibit more promising results than traditional pipelined methods in general domain. However, they are inappropriate for the biomedical domain due to numerous overlapping relations in biomedical text. To alleviate the problem, we propose a neural network-based joint learning approach for biomedical entity and relation extraction. In this approach, a novel tagging scheme that takes into account overlapping relations is proposed. Then the Att-BiLSTM-CRF model is built to jointly extract the entities and their relations with our extraction rules. Moreover, the contextualized ELMo representations pretrained on biomedical text are used to further improve the performance. Experimental results on biomedical corpora show that our method can significantly improve the performance of overlapping relation extraction and achieves the state-of-the-art performance.