Automatic labeling hierarchical topics

Xian Ling Mao, Zhao Yan Ming, Zheng Jun Zha, Tat Seng Chua, Hongfei Yan*, Xiaoming Li

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

34 引用 (Scopus)

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.

源语言英语
主期刊名CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
2383-2386
页数4
DOI
出版状态已出版 - 2012
已对外发布
活动21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, 美国
期限: 29 10月 20122 11月 2012

出版系列

姓名ACM International Conference Proceeding Series

会议

会议21st ACM International Conference on Information and Knowledge Management, CIKM 2012
国家/地区美国
Maui, HI
时期29/10/122/11/12

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