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


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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  1. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, et al. (2016) Tensorflow: A system for large-scale machine learning. In: 12th \(\{\)USENIX\(\}\) Symposium on Operating Systems Design and Implementation (\(\{\)OSDI\(\}\) 16), pp 265–283

  2. Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84

    Article  Google Scholar 

  3. Blei DM, McAuliffe JD (2010) Supervised topic models. arXiv preprint arXiv:1003.0783

  4. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  5. Burkhardt S, Kramer S (2019a) Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model. J Mach Learn Res 20(131):1–27

    MathSciNet  MATH  Google Scholar 

  6. Burkhardt S, Kramer S (2019b) A survey of multi-label topic models. ACM SIGKDD Explorations Newsl 21(2):61–79. https://doi.org/10.1145/3373464.3373474

    Article  Google Scholar 

  7. Card D, Tan C, Smith NA (2018) Neural models for documents with metadata. arXiv preprint arXiv:1705.09296

  8. Chaudhary Y, Gupta P, Saxena K, Kulkarni V, Runkler T, Schütze H (2020) Topicbert for energy efficient document classification. arXiv preprint arXiv:2010.16407

  9. Chen J, Zhang K, Zhou Y, Chen Z, Liu Y, Tang Z, Yin L (2019) A novel topic model for documents by incorporating semantic relations between words. Soft Comput 24(15):11407–11423. https://doi.org/10.1007/s00500-019-04604-0

    Article  Google Scholar 

  10. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  11. Engelen JEV, Hoos HH (2019) A survey on semi-supervised learning. Mach Learn 109(2):373–440. https://doi.org/10.1007/s10994-019-05855-6

    MathSciNet  Article  MATH  Google Scholar 

  12. Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: European conference on information retrieval, Springer, pp 345–359

  13. Grandvalet Y, Bengio Y (2004) Semi-supervised learning by entropy minimization. Adv Neural Inf Process Syst 17:529–536

    Google Scholar 

  14. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(Supplement 1):5228–5235. https://doi.org/10.1073/pnas.0307752101

    Article  Google Scholar 

  15. Hennig P, Stern D, Herbrich R, Graepel T (2012) Kernel topic models. In: Artificial Intelligence and Statistics, pp 511–519

  16. Joo W, Lee W, Park S, Moon IC (2020) Dirichlet variational autoencoder. Pattern Recogn 107:107107514. https://doi.org/10.1016/j.patcog.2020.107514

    Article  Google Scholar 

  17. Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114

  18. Kingma DP, Rezende DJ, Mohamed S, Welling M (2014) Semi-supervised learning with deep generative models. arXiv preprint arXiv:1406.5298

  19. Ma T, Pan Q, Rong H, Qian Y, Tian Y, Al-Nabhan N (2021) T-bertsum: Topic-aware text summarization based on bert. IEEE Transactions on Computational Social Systems

  20. Miao Y, Yu L, Blunsom P (2016) Neural variational inference for text processing. In: International conference on machine learning, pp 1727–1736

  21. Palani S, Rajagopal P, Pancholi S (2021) T-bert–model for sentiment analysis of micro-blogs integrating topic model and bert. arXiv preprint arXiv:2106.01097

  22. Pavlinek M, Podgorelec V (2017) Text classification method based on self-training and lda topic models. Expert Syst Appl 80:83–93

    Article  Google Scholar 

  23. Peinelt N, Nguyen D, Liakata M (2020) tbert: Topic models and bert joining forces for semantic similarity detection. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 7047–7055

  24. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2019) Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683

  25. Ramage D, Hall D, Nallapati R, Manning CD (2009) Labeled lda: A supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 conference on empirical methods in natural language processing, pp 248–256

  26. Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082

  27. Rubin TN, Chambers A, Smyth P, Steyvers M (2011) Statistical topic models for multi-label document classification. Mach Learn 88(1–2):157–208. https://doi.org/10.1007/s10994-011-5272-5

    MathSciNet  Article  MATH  Google Scholar 

  28. Sheikhpour R, Sarram MA, Gharaghani S, Chahooki MAZ (2017) A survey on semi-supervised feature selection methods. Pattern Recogn 64:141–158. https://doi.org/10.1016/j.patcog.2016.11.003

    Article  MATH  Google Scholar 

  29. Soleimani H, Miller DJ (2016) Semi-supervised multi-label topic models for document classification and sentence labeling. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp 105–114

  30. Soleimani H, Miller DJ (2017) Exploiting the value of class labels on high-dimensional feature spaces: topic models for semi-supervised document classification. Pattern Anal Appl 22(2):299–309. https://doi.org/10.1007/s10044-017-0629-4

    MathSciNet  Article  Google Scholar 

  31. Srivastava A, Sutton C (2017) Autoencoding variational inference for topic models. arXiv preprint arXiv:1703.01488

  32. Teh Y, Newman D, Welling M (2006) A collapsed variational bayesian inference algorithm for latent dirichlet allocation. Adv Neural Inf Process Syst 19:1353–1360

    Google Scholar 

  33. Ueda N, Saito K (2002) Parametric mixture models for multi-labeled text. Adv Neural Inf Process Syst 15:737–744

    Google Scholar 

  34. Wang C, Blei D, Li FF (2009) Simultaneous image classification and annotation. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1903–1910

  35. Wang D, Thint M, Al-Rubaie A (2012) Semi-supervised latent dirichlet allocation and its application for document classification. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, IEEE, vol 3, pp 306–310

  36. Wang R, Hu X, Zhou D, He Y, Xiong Y, Ye C, Xu H (2020a) Neural topic modeling with bidirectional adversarial training. arXiv preprint arXiv:2004.12331

  37. Wang W, Guo B, Shen Y, Yang H, Chen Y, Suo X (2020b) Twin labeled LDA: a supervised topic model for document classification. Appl Intell 50(12):4602–4615. https://doi.org/10.1007/s10489-020-01798-x

    Article  Google Scholar 

  38. Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3):229–256

    MATH  Google Scholar 

  39. Xu W, Sun H, Deng C, Tan Y (2017) Variational autoencoder for semi-supervised text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31(1)

  40. Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retrieval 1(1–2):69–90

    Article  Google Scholar 

  41. Zhang H, Chen B, Guo D, Zhou M (2018) Whai: Weibull hybrid autoencoding inference for deep topic modeling. arXiv preprint arXiv:1803.01328

  42. Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Adv Neural Inf Process Syst 28:649–657

    Google Scholar 

  43. Zhang Y, Wei W (2014) A jointly distributed semi-supervised topic model. Neurocomputing 134:38–45

    Article  Google Scholar 

  44. Zhou C, Ban H, Zhang J, Li Q, Zhang Y (2020) Gaussian mixture variational autoencoder for semi-supervised topic modeling. IEEE Access 8:106843–106854. https://doi.org/10.1109/access.2020.3001184

    Article  Google Scholar 

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This work was supported in part by the National Natural Science Foundation of China under Grant No. 61772352; the Science and Technology Planning Project of Sichuan Province under Grant No. 2019YFG0400, 2018GZDZX0031, 2018GZDZX0004, 2017GZDZX0003, 2018JY0182, 19ZDYF1286.

Author information




Wei Wang was involved in conceptualization and writing—original draft. Wei Wang and Bing Guo were involved in methodology and investigation. Wei Wang, Bing Guo, Yaosen Chen and Xinhua Suo were involved in formal analysis. Han Yang and Wei Wang performed software. Bing Guo, Yan Shen and Han Yang were involved in writing—review and editing. Bing Guo and Yan Shen were involved in supervision and funding acquisition. Han Yang, Xinhua Suo and Yaosen Chen were involved in validation. Han Yang, Wei Wang and Yaosen Chen were involved in datasets preparation Yaosen Chen was involved in visualization.

Corresponding author

Correspondence to Bing Guo.

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The authors declared that they have no conflicts of interest/competing interests to this work.

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The datasets used during the current study are publicly available.

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The source code used in the current study is available from the first author or corresponding author on reasonable request.

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Wang, W., Guo, B., Shen, Y. et al. Neural labeled LDA: a topic model for semi-supervised document classification. Soft Comput 25, 14561–14571 (2021). https://doi.org/10.1007/s00500-021-06310-2

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  • Neural topic model
  • Semi-supervised learning
  • Document classification