| Subject: | 固体力学
|
| Title: | Smoothing LDA Model for Text Categorization |
| Author: | Li Wenbo
; Le Sun
; Yuanyong Feng
; Dakun Zhang
|
| Source: | Lecture Notes in Computer Science
|
| Conference Name: | 待定
|
| Conference Date: | 39766
|
| Issued Date: | 2008
|
| Conference Place: | Harbin,China
|
| Keyword: | Text Categorization
; Latent Dirichlet Allocation
; Smoothing
; Graphical Model
|
| Publisher: | 科学出版社
|
| Publish Place: | 北京
|
| Indexed Type: | EI,ISTP
|
| ISSN: | 1234-5678
|
| Abstract: | 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. |
| Language: | 英语
|
| Content Type: | 会议论文
|
| URI: | http://ir.iscas.ac.cn/handle/311060/808
|
| Appears in Collections: | 基础软件国家工程研究中心_会议论文
|
| File Name/ File Size |
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Version |
Access |
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| lwb-conf-01.pdf（389KB） | -- | -- | 限制开放 | -- | 联系获取全文 |
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| Recommended Citation: | |
Li Wenbo,Le Sun,Yuanyong Feng,et al. Smoothing LDA Model for Text Categorization[C]. 见:待定. Harbin,China. 39766.
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