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Metadata-Based Clustered Multi-task Learning for Thread Mining in Web Communities

  • Qiang YouEmail author
  • Ou Wu
  • Guan Luo
  • Weiming Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)

Abstract

With user-generated content explosively growing, how to find valuable posts from discussion threads in web communities becomes a hot topic. Although many learning algorithms have been proposed for mining the thread contents, there are still two problems that are not effectively considered. First, the learning algorithms are usually complicated so as to deal with various kinds of threads in web communities, which damages the generalization performance of the algorithms and takes the risk of overfitting to the learning models. Second, the small sample size problem exists when the training data for learning is divided into many isolated groups and each group is trained separately in order to avoid overfitting. In this paper, we propose a metadata-based clustered multi-task learning method, which takes full use of the metadata of threads and fuses it in the multi-task learning based on a divide-and-learn strategy. Our method provides an effective solution to the above problems by finding the geometric structure or context of semantics of threads in web communities and constructing the relations among training thread groups and their corresponding learning tasks. In addition, a soft-assigned clustered multi-task learning model is employed. Our experimental results show the effectiveness of our method.

Keywords

Metadata Thread mining Divide-and-learn Clustered multi-task learning Web community 

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References

  1. 1.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  2. 2.
    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
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5), 604–632 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)Google Scholar
  6. 6.
    Cong, G., Wang, L., Lin, C.Y., Song, Y.I., Sun, Y.: Finding question-answer pairs from online forums. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 467–474. ACM (2008)Google Scholar
  7. 7.
    Blei, D.M., Moreno, P.J.: Topic segmentation with an aspect hidden markov model. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343–348. ACM (2001)Google Scholar
  8. 8.
    Shen, D., Yang, Q., Sun, J.T., Chen, Z.: Thread detection in dynamic text message streams. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 35–42. ACM (2006)Google Scholar
  9. 9.
    Lin, C., Yang, J.M., Cai, R., Wang, X.J., Wang, W.: Simultaneously modeling semantics and structure of threaded discussions: a sparse coding approach and its applications. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 131–138. ACM (2009)Google Scholar
  10. 10.
    Poh, N., Kittler, J., Bourlai, T.: Quality-based score normalization with device qualitative information for multimodal biometric fusion. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(3), 539–554 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Poh, N., Kittler, J.: A unified framework for biometric expert fusion incorporating quality measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1), 3–18 (2012)CrossRefGoogle Scholar
  12. 12.
    Wu, O., Hu, R., Mao, X., Hu, W.: Quality-based learning for web data classification. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)Google Scholar
  13. 13.
    Fu, Z., Robles-Kelly, A., Zhou, J.: Mixing linear svms for nonlinear classification. IEEE Transactions on Neural Networks 21(12), 1963–1975 (2010)CrossRefGoogle Scholar
  14. 14.
    Gu, Q., Han, J.: Clustered support vector machines. In: Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, pp. 307–315 (2013)Google Scholar
  15. 15.
    Ben-David, S., Schuller, R.: Exploiting task relatedness for multiple task learning. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 567–580. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  16. 16.
    Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing features: efficient boosting procedures for multiclass object detection. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II-762. IEEEGoogle Scholar
  17. 17.
    Ando, R.K., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research 6, 1817–1853 (2005)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–117. ACM (2004)Google Scholar
  19. 19.
    Thrun, S., O’Sullivan, J.: Clustering learning tasks and the selective cross-task transfer of knowledge. Springer (1998)Google Scholar
  20. 20.
    Bakker, B., Heskes, T.: Task clustering and gating for bayesian multitask learning. The Journal of Machine Learning Research 4, 83–99 (2003)zbMATHGoogle Scholar
  21. 21.
    Kim, S., Xing, E.P.: Tree-guided group lasso for multi-task regression with structured sparsity. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 543–550 (2010)Google Scholar
  22. 22.
    Chen, J., Liu, J., Ye, J.: Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Transactions on Knowledge Discovery from Data (TKDD) 5(4), 22 (2012)CrossRefGoogle Scholar
  23. 23.
    Chen, J., Tang, L., Liu, J., Ye, J.: A convex formulation for learning shared structures from multiple tasks. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 137–144. ACM (2009)Google Scholar
  24. 24.
    Xue, Y., Liao, X., Carin, L., Krishnapuram, B.: Multi-task learning for classification with dirichlet process priors. The Journal of Machine Learning Research 8, 35–63 (2007)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Jacob, L., Bach, F., Vert, J.P., et al.: Clustered multi-task learning: a convex formulation. In: NIPS, vol. 21, pp. 745–752 (2008)Google Scholar
  26. 26.
    Zhou, J., Chen, J., Ye, J.: Clustered multi-task learning via alternating structure optimization. In: NIPS, pp. 702–710 (2011)Google Scholar
  27. 27.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proceedings of the VLDB Endowment 2(1), 718–729 (2009)CrossRefGoogle Scholar
  28. 28.
    Cheng, H., Zhou, Y., Yu, J.X.: Clustering large attributed graphs: A balance between structural and attribute similarities. ACM Transactions on Knowledge Discovery from Data (TKDD) 5(2), 12 (2011)CrossRefGoogle Scholar
  29. 29.
    Von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Wu, O., Hu, W., Maybank, S.J., Zhu, M., Li, B.: Efficient clustering aggregation based on data fragments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(3), 913–926 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.CAS Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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