Please use this identifier to cite or link to this item: https://doi.org/10.1145/2396761.2398646
Title: Automatic labeling hierarchical topics
Authors: Mao, X.-L.
Ming, Z.-Y. 
Zha, Z.-J. 
Chua, T.-S. 
Yan, H.
Li, X.
Keywords: statistical topic models
topic model labeling
Issue Date: 2012
Source: Mao, X.-L.,Ming, Z.-Y.,Zha, Z.-J.,Chua, T.-S.,Yan, H.,Li, X. (2012). Automatic labeling hierarchical topics. ACM International Conference Proceeding Series : 2383-2386. ScholarBank@NUS Repository. https://doi.org/10.1145/2396761.2398646
Abstract: 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.
Source Title: ACM International Conference Proceeding Series
URI: http://scholarbank.nus.edu.sg/handle/10635/41919
ISBN: 9781450311564
DOI: 10.1145/2396761.2398646
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