Exploring developments of the AI field from the perspective of methods, datasets, and metrics

doi:10.1016/j.ipm.2022.103157

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统认知与决策实验室

	Exploring developments of the AI field from the perspective of methods, datasets, and metrics
	Yao, Rujing 3,4; Ye, Yingchun 3; Zhang, Ji 2; Li, Shuxiao 1; Wu, Ou 3
刊名	INFORMATION PROCESSING & MANAGEMENT
	2023-03-01
卷号	60 期号:2 页码:21
关键词	AI literature Named entity recognition Self-paced learning Entity-level analysis
ISSN号	0306-4573
DOI	10.1016/j.ipm.2022.103157
通讯作者	Wu, Ou(wuou@tju.edu.cn)
英文摘要	The knowledge contained in academic literature is interesting to mine. Inspired by the idea of molecular markers tracing in the field of biochemistry, three named entities, namely, methods, datasets, and metrics, are extracted and used as artificial intelligence (AI) markers for AI literature. These entities can be used to trace the research process described in the bodies of papers, which opens up new perspectives for seeking and mining more valuable academic information. Firstly, the named entity recognition model is used to extract AI markers from large-scale AI literature. A multi-stage self-paced learning strategy (MSPL) is proposed to address the negative influence of hard and noisy samples on the model training. Secondly, original papers are traced for AI markers. Statistical and propagation analyses are performed based on the tracing results. Finally, the co-occurrences of AI markers are used to achieve clustering. The evolution within method clusters is explored. The above-mentioned mining based on AI markers yields many significant findings. For example, the propagation rate of the datasets gradually increases. The methods proposed by China in recent years have an increasing influence on other countries.
WOS关键词	FUNCTIONAL STRUCTURE IDENTIFICATION ; NAMED ENTITY RECOGNITION ; FULL-TEXT ; ACADEMIC ARTICLES ; SOFTWARE ; CITATION ; DOMAIN
WOS研究方向	Computer Science ; Information Science & Library Science
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000900807500017
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51147]
专题	复杂系统认知与决策实验室
通讯作者	Wu, Ou
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Zhejiang Lab, Hangzhou, Peoples R China 3.Tianjin Univ, Ctr Appl Math, Tianjin, Peoples R China 4.Nankai Univ, Business Sch, Dept Informat Resources Management, Tianjin, Peoples R China
推荐引用方式 GB/T 7714	Yao, Rujing,Ye, Yingchun,Zhang, Ji,et al. Exploring developments of the AI field from the perspective of methods, datasets, and metrics[J]. INFORMATION PROCESSING & MANAGEMENT,2023,60(2):21.
APA	Yao, Rujing,Ye, Yingchun,Zhang, Ji,Li, Shuxiao,&Wu, Ou.(2023).Exploring developments of the AI field from the perspective of methods, datasets, and metrics.INFORMATION PROCESSING & MANAGEMENT,60(2),21.
MLA	Yao, Rujing,et al."Exploring developments of the AI field from the perspective of methods, datasets, and metrics".INFORMATION PROCESSING & MANAGEMENT 60.2(2023):21.