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Sentence level topic models for associated topics extraction

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Abstract

In LDA model, independence assumptions in the Dirichlet distribution of the topic proportions lead to the inability to model the connections between topics. Some researchers have attempted to break them and thus obtained more powerful topic models. Following this strategy, by using an association matrix to measure the association between latent topics, we develop an associated topic model (ATM), in which consecutive sentences are considered important and the topic assignments for words are jointly determined by the association matrix and the sentence level topic distributions, instead of the document-specific topic distributions only. This approach gives a more realistic modeling of latent topic connections where the presence of a topic may be connected with the presence of another. We derive a collapsed Gibbs sampling algorithm for inference and parameter estimation for the ATM. The experimental results demonstrate that the ATM gives a more practical interpretation and is capable of learning more associated topics.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61672161, 61272480), and in part by the grants of Australian Research Council(DP170104747).

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Haixin Jiang & Limeng Zhang

  2. Swinburne University of Technology, Hawthorn, Australia

    Rui Zhou

  3. Victoria University, Footscray, Australia

    Hua Wang & Yanchun Zhang

  4. Fudan University, Shanghai, China

    Yanchun Zhang

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  1. Haixin Jiang
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  2. Rui Zhou
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  3. Limeng Zhang
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  4. Hua Wang
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Correspondence to Haixin Jiang.

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This article belongs to the Topical Collection: Special Issue on Web and Big Data

Guest Editors: Junjie Yao, Bin Cui, Christian S. Jensen, and Zhe Zhao

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Jiang, H., Zhou, R., Zhang, L. et al. Sentence level topic models for associated topics extraction. World Wide Web 22, 2545–2560 (2019). https://doi.org/10.1007/s11280-018-0639-1

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  • DOI: https://doi.org/10.1007/s11280-018-0639-1

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