Twin labeled LDA: a supervised topic model for document classification

Wei Wang; Bing Guo; Yan Shen; Han Yang; Yaosen Chen; Xinhua Suo

doi:10.1007/s10489-020-01798-x

Twin labeled LDA: a supervised topic model for document classification

Applied Intelligence ◽

10.1007/s10489-020-01798-x ◽

2020 ◽

Vol 50 (12) ◽

pp. 4602-4615

Author(s):

Wei Wang ◽

Bing Guo ◽

Yan Shen ◽

Han Yang ◽

Yaosen Chen ◽

...

Keyword(s):

Topic Model ◽

Document Classification

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Neural labeled LDA: a topic model for semi-supervised document classification

Soft Computing ◽

10.1007/s00500-021-06310-2 ◽

2021 ◽

Author(s):

Wei Wang ◽

Bing Guo ◽

Yan Shen ◽

Han Yang ◽

Yaosen Chen ◽

...

Keyword(s):

Topic Model ◽

Document Classification

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A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Symmetry ◽

10.3390/sym11121486 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1486

Author(s):

Zhinan Gou ◽

Zheng Huo ◽

Yuanzhen Liu ◽

Yi Yang

Keyword(s):

Topic Modeling ◽

Topic Model ◽

State Of The Art ◽

Topic Models ◽

Document Classification ◽

Experimental Results ◽

Tag Recommendation ◽

Term Frequency ◽

Series Of Experiments ◽

Dirichlet Prior

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.

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Labelset topic model for multi-label document classification

Journal of Intelligent Information Systems ◽

10.1007/s10844-014-0352-1 ◽

2014 ◽

Vol 46 (1) ◽

pp. 83-97 ◽

Cited By ~ 4

Author(s):

Ximing Li ◽

Jihong Ouyang ◽

Xiaotang Zhou

Keyword(s):

Topic Model ◽

Document Classification

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A link-bridged topic model for cross-domain document classification

Information Processing & Management ◽

10.1016/j.ipm.2013.05.002 ◽

2013 ◽

Vol 49 (6) ◽

pp. 1181-1193 ◽

Cited By ~ 11

Author(s):

Pei Yang ◽

Wei Gao ◽

Qi Tan ◽

Kam-Fai Wong

Keyword(s):

Topic Model ◽

Document Classification ◽

Cross Domain

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Neural labeled LDA: a topic model for semi-supervised document classification

10.21203/rs.3.rs-171202/v1 ◽

2021 ◽

Author(s):

Wei Wang ◽

Bing Guo ◽

Yan Shen ◽

Han Yang ◽

Yaosen Chen ◽

...

Keyword(s):

Supervised Learning ◽

Topic Modeling ◽

Supervised Classification ◽

Topic Model ◽

Classification Performance ◽

Document Classification ◽

Classification Problems ◽

Modeling Approaches ◽

Proposed Model ◽

Density Assumption

Abstract Recently, some statistical topic modeling approaches based on LDA have been applied in the field of supervised document classification, where the model generation procedure incorporates prior knowledge to improve the classification performance. However, these customizations of topic modeling are limited by the cumbersome derivation of a specific inference algorithm for each modification. In this paper, we propose a new supervised topic modeling approach for document classification problems, Neural Labeled LDA (NL-LDA), which builds on the VAE framework, and designs a special generative network to incorporate prior information. The proposed model can support semi-supervised learning based on the manifold assumption and low-density assumption. Meanwhile, NL-LDA has a consistent and concise inference method while semi-supervised learning and predicting. Quantitative experimental results demonstrate our model has outstanding performance on supervised document classification relative to the compared approaches, including traditional statistical and neural topic models. Specially, the proposed model can support both single-label and multi-label document classification. The proposed NL-LDA performs significantly well on semi-supervised classification, especially under a small amount of labeled data. Further comparisons with related works also indicate our model is competitive with state-of-the-art topic modeling approaches on semi-supervised classification.

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