NDLI: Automatic labeling of multinomial topic models

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Automatic labeling of multinomial topic models

Content Provider	ACM Digital Library
Author	Mei, Qiaozhu Shen, Xuehua Zhai, ChengXiang
Abstract	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 learned through all kinds of topic models such as PLSA, LDA, and their variations.
Starting Page	490
Ending Page	499
Page Count	10
File Format	PDF
ISBN	9781595936097
DOI	10.1145/1281192.1281246
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical topic models Topic model labeling Multinomial distribution
Content Type	Text
Resource Type	Article

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