QQ客服 官方微博 反馈留言

	Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis
	Hu Kai; Luo Qing; Qi Kunlun; Yang Siluo; Mao Jin; Fu Xiaokang; Zheng Jie; Wu Huayi; Guo Ya; Zhu Qibing
刊名	INFORMATION PROCESSING & MANAGEMENT
	2019
卷号	56期号:4
关键词	Semantic relatedness Topic evolution Spatial clustering Spatial autocorrelation Word2Vec
ISSN号	0306-4573
DOI	10.1016/j.ipm.2019.02.014
URL标识	查看原文
收录类别	EI ; SCIE ; SSCI
语种	英语
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/4288822
专题	武汉大学
推荐引用方式 GB/T 7714	Hu Kai,Luo Qing,Qi Kunlun,et al. Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis[J]. INFORMATION PROCESSING & MANAGEMENT,2019,56(4).
APA	Hu Kai.,Luo Qing.,Qi Kunlun.,Yang Siluo.,Mao Jin.,...&Zhu Qibing.(2019).Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis.INFORMATION PROCESSING & MANAGEMENT,56(4).
MLA	Hu Kai,et al."Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis".INFORMATION PROCESSING & MANAGEMENT 56.4(2019).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。