Please use this identifier to cite or link to this item:
|Title:||Automatic labeling hierarchical topics|
|Keywords:||statistical topic models|
topic model labeling
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2017
checked on Dec 16, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.