Named Entity Recognition in Chinese Medical Literature Using Pretraining Models

doi:10.1155/2020/8812754

	Named Entity Recognition in Chinese Medical Literature Using Pretraining Models
	Wang, Yu 1,2; Sun, Yining 1,2; Ma, Zuchang 1; Gao, Lisheng 1; Xu, Yang 1
刊名	SCIENTIFIC PROGRAMMING
	2020-09-09
卷号	2020
ISSN号	1058-9244
DOI	10.1155/2020/8812754
通讯作者	Sun, Yining(ynsun@iim.ac.cn)
英文摘要	The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representations with the prior semantic knowledge on large-scale unlabelled corpora, have achieved state-of-the-art results for a wide variety of Natural Language Processing (NLP) tasks. However, the capabilities of pretraining models have not been fully exploited, and applications of other pretraining models except BERT in specific domains, such as NER in Chinese medical literature, are also of interest. In this paper, we enhance the performance of NER in Chinese medical literature using pretraining models. First, we propose a method of data augmentation by replacing the words in the training set with synonyms through the Mask Language Model (MLM), which is a pretraining task. Then, we consider NER as the downstream task of the pretraining model and transfer the prior semantic knowledge obtained during pretraining to it. Finally, we conduct experiments to compare the performances of six pretraining models (BERT, BERT-WWM, BERT-WWM-EXT, ERNIE, ERNIE-tiny, and RoBERTa) in recognizing named entities from Chinese medical literature. The effects of feature extraction and fine-tuning, as well as different downstream model structures, are also explored. Experimental results demonstrate that the method of data augmentation we proposed can obtain meaningful improvements in the performance of recognition. Besides, RoBERTa-CRF achieves the highestF1-score compared with the previous methods and other pretraining models.
资助项目	major special project of Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KFJ-STS-ZDTP-079]
WOS研究方向	Computer Science
语种	英语
出版者	HINDAWI LTD
WOS记录号	WOS:000574404000001
资助机构	major special project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/104341]
专题	中国科学院合肥物质科学研究院
通讯作者	Sun, Yining
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Anhui Prov Key Lab Med Phys & Technol, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yu,Sun, Yining,Ma, Zuchang,et al. Named Entity Recognition in Chinese Medical Literature Using Pretraining Models[J]. SCIENTIFIC PROGRAMMING,2020,2020.
APA	Wang, Yu,Sun, Yining,Ma, Zuchang,Gao, Lisheng,&Xu, Yang.(2020).Named Entity Recognition in Chinese Medical Literature Using Pretraining Models.SCIENTIFIC PROGRAMMING,2020.
MLA	Wang, Yu,et al."Named Entity Recognition in Chinese Medical Literature Using Pretraining Models".SCIENTIFIC PROGRAMMING 2020(2020).