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	GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts
	Liang, Wenxin; Feng, Ran; Liu, Xinyue; Li, Yuangang; Zhang, Xianchao
刊名	IEEE ACCESS
	2018
卷号	6页码:43612-43621
关键词	Text mining context modeling natural language processing topic model short text
ISSN号	2169-3536
URL标识	查看原文
WOS记录号	[DB:DC_IDENTIFIER_WOSID]
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/3257019
专题	大连理工大学
作者单位	1.Chongqing Univ Posts & Telecommun, Sch Software Engn, Chongqing 400065, Peoples R China. 2.Dalian Univ Technol, Sch Software, Dalian 116620, Peoples R China. 3.Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai 200433, Peoples R China.
推荐引用方式 GB/T 7714	Liang, Wenxin,Feng, Ran,Liu, Xinyue,et al. GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts[J]. IEEE ACCESS,2018,6:43612-43621.
APA	Liang, Wenxin,Feng, Ran,Liu, Xinyue,Li, Yuangang,&Zhang, Xianchao.(2018).GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts.IEEE ACCESS,6,43612-43621.
MLA	Liang, Wenxin,et al."GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts".IEEE ACCESS 6(2018):43612-43621.

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