A WORD POSITION-RELATED LDA MODEL

doi:10.1142/S0218001411008890

	A WORD POSITION-RELATED LDA MODEL
	Zhai, Lidong 1; Ding, Zhaoyun 2; Jia, Yan 2; Zhou, Bin 2
	2011-09-01
发表期刊	INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
ISSN	0218-0014
卷号	25 期号:6 页码:909-925
摘要	LDA (Latent Dirichlet Allocation) proposed by Blei is a generative probabilistic model of a corpus, where documents are represented as random mixtures over latent topics, and each topic is characterized by a distribution over words, but not the attributes of word positions of every document in the corpus. In this paper, a Word Position-Related LDA Model is proposed taking into account the attributes of word positions of every document in the corpus, where each word is characterized by a distribution over word positions. At the same time, the precision of the topic-word's interpretability is improved by integrating the distribution of the word-position and the appropriate word degree, taking into account the different word degree in the different word positions. Finally, a new method, a size-aware word intrusion method is proposed to improve the ability of the topic-word's interpretability. Experimental results on the NIPS corpus show that the Word Position-Related LDA Model can improve the precision of the topic-word's interpretability. And the average improvement of the precision in the topic-word's interpretability is about 9.67%. Also, the size-aware word intrusion method can interpret the topic-word's semantic information more comprehensively and more effectively through comparing the different experimental data.
关键词	LDA probabilistic topic models word position word degree word intrusion
DOI	10.1142/S0218001411008890
收录类别	SCI
语种	英语
资助项目	Basic Research Program of China (973 Program)[2007CB311100] ; National Natural Science Foundation of China[61003261] ; National Natural Science Foundation of China[60933005] ; National Natural Science Foundation of China[60873204] ; National Natural Science Foundation of China[12505] ; National Natural Science Foundation of China[2011AA010702]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000295128400006
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/13164
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhai, Lidong
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Res Ctr Informat Secur, Beijing, Peoples R China 2.Natl Univ Def Technol, Sch Comp, Changsha, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Zhai, Lidong,Ding, Zhaoyun,Jia, Yan,et al. A WORD POSITION-RELATED LDA MODEL[J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,2011,25(6):909-925.
APA	Zhai, Lidong,Ding, Zhaoyun,Jia, Yan,&Zhou, Bin.(2011).A WORD POSITION-RELATED LDA MODEL.INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE,25(6),909-925.
MLA	Zhai, Lidong,et al."A WORD POSITION-RELATED LDA MODEL".INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 25.6(2011):909-925.