Automatic labeling of multinomial topic models

10 years 9 months ago
Automatic labeling of multinomial topic models
Multinomial distributions over words are frequently used to model topics in text collections. A common, major challenge in applying all such topic models to any text mining problem is to label a multinomial topic model accurately so that a user can interpret the discovered topic. So far, such labels have been generated manually in a subjective way. In this paper, we propose probabilistic approaches to automatically labeling multinomial topic models in an objective way. We cast this labeling problem as an optimization problem involving minimizing Kullback-Leibler divergence between word distributions and maximizing mutual information between a label and a topic model. Experiments with user study have been done on two text data sets with different genres. The results show that the proposed labeling methods are quite effective to generate labels that are meaningful and useful for interpreting the discovered topic models. Our methods are general and can be applied to labeling topics learn...
Qiaozhu Mei, Xuehua Shen, ChengXiang Zhai
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Qiaozhu Mei, Xuehua Shen, ChengXiang Zhai
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