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Cited by 384

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Automatic labeling of multinomial topic models

Mei, Qiaozhu, Shen, Xuehua, Zhai, ChengXiang·2007·Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
DOI 10.1145/1281192.1281246
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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.

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Metadata

Authors
Mei, Q., Shen, X., & Zhai, C.
Venue
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher
ACM
Published
2007-08-12
Pages
490-499
DOI
10.1145/1281192.1281246
OpenAlex
W2113855231
Cited by
384
References
31
FWCI
9.32
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Automatic labeling of multinomial topic models. https://app.cochat.ai/papers/10.1145/1281192.1281246
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Mei, Q., Shen, X., & Zhai, C. (2007). Automatic labeling of multinomial topic models. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1281192.1281246

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