Online Subset Topic Modeling for Interactive Documents Exploration

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Data exploration over text databases is an important problem. In an exploration scenario, users would find something useful without previously knowing what exactly they are looking for, until the time they identify them. Therefore, labor-intensive efforts are often required, since users have to review the overview (or detail) results of ad-hoc queries and adjust the queries (e.g., zoom or filter) continuously. Probabilistic topic models are often adopted as a solution to provide the overview for a given text collection, since it could discover the underlying thematic structures of unstructured text data. However, training a topic model for a selected document collection is time consuming. Moreover, frequent model retraining would be introduced by continuous query-adjusting, which leads to large amount of time wasting and therefore is unsuitable for online exploration. To remedy this problem, this paper presents STMS, an algorithm for constructing topic structures in document subsets efficiently. STMS accelerates the process of subset modeling by leveraging global precomputation and applying an efficient sampling-based inference algorithm. The experiments on real world datasets show that STMS achieves orders of magnitude speed-ups than standard topic model, while remaining comparable in terms of modeling quality.

Keywords

Subset topic modeling OLAP Exploratory analysis 

References

  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)MATHGoogle Scholar
  2. 2.
    Cao, N., Sun, J., Lin, Y.R., Gotz, D., Liu, S., Qu, H.: Facetatlas: multifaceted visualization for rich text corpora. IEEE Trans. Vis. Comput. Graph. 16(6), 1172–1181 (2010)CrossRefGoogle Scholar
  3. 3.
    Gardner, M.J., Lutes, J., Lund, J., Hansen, J., Walker, D., Ringger, E., Seppi, K.: The topic browser: an interactive tool for browsing topic models. In: NIPS Workshop on Challenges of Data Visualization, vol. 2 (2010)Google Scholar
  4. 4.
    Görg, C., Liu, Z., Kihm, J., Choo, J., Park, H., Stasko, J.: Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw. IEEE Trans. Vis. Comput. Graph. 19(10), 1646–1663 (2013)CrossRefGoogle Scholar
  5. 5.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Nat. Acad. Sci. 101(suppl 1), 5228–5235 (2004)CrossRefGoogle Scholar
  6. 6.
    Hoffman, M., Bach, F.R., Blei, D.M.: Online learning for latent dirichlet allocation. In: Advances in Neural Information Processing Systems, pp. 856–864 (2010)Google Scholar
  7. 7.
    Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)Google Scholar
  8. 8.
    Li, A.Q., Ahmed, A., Ravi, S., Smola, A.J.: Reducing the sampling complexity of topic models. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 891–900. ACM (2014)Google Scholar
  9. 9.
    Newman, D., Asuncion, A., Smyth, P., Welling, M.: Distributed algorithms for topic models. J. Mach. Learn. Res. 10(Aug), 1801–1828 (2009)MathSciNetMATHGoogle Scholar
  10. 10.
    Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., Welling, M.: Fast collapsed gibbs sampling for latent dirichlet allocation. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 569–577. ACM (2008)Google Scholar
  11. 11.
    Yin, J., Wang, J.: A dirichlet multinomial mixture model-based approach for short text clustering. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242. ACM (2014)Google Scholar
  12. 12.
    Yuan, J., Gao, F., Ho, Q., Dai, W., Wei, J., Zheng, X., Xing, E.P., Liu, T.Y., Ma, W.Y.: LightLDA: big topic models on modest computer clusters. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1351–1361. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Fudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Data ScienceShanghaiChina
  3. 3.Shanghai Insititute of Intelligent Electronics and SystemsShanghaiChina

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