Smoothing LDA Model for Text Categorization

	Smoothing LDA Model for Text Categorization
	Li Wenbo; Le Sun; Yuanyong Feng; Dakun Zhang
	2008
会议名称	待定
会议录名称	Lecture Notes in Computer Science
页码	83-94
会议日期	39766
会议地点	Harbin,China
收录类别	EI,ISTP
出版地	北京
出版者	科学出版社
ISSN	1234-5678
摘要	Abstract. Latent Dirichlet Allocation (LDA) is a document level language model. In general, LDA employ the symmetry Dirichlet distribution as prior of the topic-words’ distributions to implement model smoothing. In this paper, we propose a data-driven smoothing strategy in which probability mass is allocated from smoothing-data to latent variables by the intrinsic inference procedure of LDA. In such a way, the arbitrariness of choosing latent variables'priors for the multi-level graphical model is overcome. Following this data-driven strategy,two concrete methods, Laplacian smoothing and Jelinek-Mercer smoothing, are employed to LDA model. Evaluations on different text categorization collections show data-driven smoothing can significantly improve the performance in balanced and unbalanced corpora.
关键词	Text Categorization Latent Dirichlet Allocation Smoothing Graphical Model
学科领域	固体力学
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/808
专题	基础软件国家工程研究中心
推荐引用方式 GB/T 7714	Li Wenbo,Le Sun,Yuanyong Feng,et al. Smoothing LDA Model for Text Categorization[C]. 北京:科学出版社,2008:83-94.

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