GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts

doi:10.1109/ACCESS.2018.2863260

CORC > 上海财经大学 > 上海财经大学

	GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts
	Liang, Wenxin 1; Feng, Ran 2; Liu, Xinyue 2; Li, Yuangang 3; Zhang, Xianchao 2
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
DOI	10.1109/ACCESS.2018.2863260
英文摘要	Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications. Due to the length limitation, conventional topic models based on document-level word co-occurrence information often fail to distill semantically coherent topics from short text collections. On the other hand, word embeddings as a powerful tool have been successfully applied in natural language processing. Word embeddings trained on large corpus are encoded with general semantic and syntactic information of words, and hence they can be leveraged to guide topic modeling for short text collections as supplementary information for sparse co-occurrence patterns. However, word embeddings are trained on large external corpus and the encoded information is not necessarily suitable for training data set of topic models, which is ignored by most existing models. In this article, we propose a novel global and local word embedding-based topic model (GLTM) for short texts. In the GLTM, we train global word embeddings from large external corpus and employ the continuous skip-gram model with negative sampling (SGNS) to obtain local word embeddings. Utilizing both the global and local word embeddings, the GLTM can distill semantic relatedness information between words which can be further leveraged by Gibbs sampler in the inference process to strengthen semantic coherence of topics. Compared with five state-of-the-art short text topic models on four real-world short text collections, the proposed GLTM exhibits the superiority in most cases.
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000443916500001
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/753]
专题	上海财经大学
通讯作者	Zhang, Xianchao
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.