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	Automatic labeling hierarchical topics
	Mao, Xian-Ling ; Ming, Zhao-Yan ; Zha, Zheng-Jun ; Chua, Tat-Seng ; Yan, Hongfei ; Li, Xiaoming
	2012
英文摘要	Recently, statistical topic modeling has been widely applied in text mining and knowledge management due to its powerful ability. A topic, as a probability distribution over words, is usually difficult to be understood. A common, major challenge in applying such topic models to other knowledge management problem is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, previous works simply treat topics individually without considering the hierarchical relation among topics, and less attention has been paid to creating a good hierarchical topic descriptors for a hierarchy of topics. In this paper, we propose two effective algorithms that automatically assign concise labels to each topic in a hierarchy by exploiting sibling and parent-child relations among topics. The experimental results show that the inter-topic relation is effective in boosting topic labeling accuracy and the proposed algorithms can generate meaningful topic labels that are useful for interpreting the hierarchical topics. ? 2012 ACM.; EI; 0
语种	英语
DOI标识	10.1145/2396761.2398646
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294571]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Mao, Xian-Ling,Ming, Zhao-Yan,Zha, Zheng-Jun,et al. Automatic labeling hierarchical topics. 2012-01-01.

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