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Computational Intelligence and Neuroscience
Volume 2015, Article ID 650527, 9 pages
http://dx.doi.org/10.1155/2015/650527
Research Article

Learning Document Semantic Representation with Hybrid Deep Belief Network

1Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Key Laboratory of Computational Linguistics, Peking University, Ministry of Education, Beijing 100871, China
3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received 26 September 2014; Revised 2 March 2015; Accepted 9 March 2015

Academic Editor: Pasi A. Karjalainen

Copyright © 2015 Yan Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. M. Rinaldi, “A content-based approach for document representation and retrieval,” in Proceedings of the 8th ACM Symposium on Document Engineering (DocEng '08), pp. 106–109, ACM, São Paulo, Brazil, September 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. K. B. Shaban, “A semantic approach for document clustering,” Journal of Software, vol. 4, no. 5, pp. 391–404, 2009. View at Google Scholar · View at Scopus
  3. R. Williams, “A computational effective document semantic representation,” in Proceedings of the Inaugural IEEE-IES Digital EcoSystems and Technologies Conference (DEST '07), pp. 410–415, IEEE, February 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” Journal of the American Society for Information Science, vol. 41, no. 6, pp. 391–407, 1990. View at Google Scholar
  5. T. Hofmann, “Probabilistic latent semantic indexing,” in Proceedings of the 22nd Annual International ACM SI-GIR Conference on Research and Development in Information Retrieval, pp. 50–57, ACM, Berkeley, Calif, USA, August 1999.
  6. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of Machine Learning Research, vol. 3, no. 4-5, pp. 993–1022, 2003. View at Google Scholar · View at Scopus
  7. N. Srivastava, R. R. Salakhutdinov, and G. E. Hinton, “Modeling documents with deep boltzmann machines,” http://arxiv.org/abs/1309.6865.
  8. H. Larochelle and S. Lauly, “A neural autoregressive topic model,” in Proceedings of the Advances in Neural Information Processing Systems, pp. 2708–2716, 2012.
  9. G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. G. E. Hinton and R. R. Salakhutdinov, “Replicated softmax: an undirected topic model,” in Advances in Neural Information Processing Systems, pp. 1607–1614, 2009. View at Google Scholar
  11. H. Larochelle and I. Murray, “The neural autoregressive distribution estimator,” Journal of Machine Learning Research, vol. 15, pp. 29–37, 2011. View at Google Scholar · View at Scopus
  12. J. Kim, J. Nam, and I. Gurevych, “Learning semantics with deep belief network for cross-language information retrieval,” in Proceedings of the COLING, pp. 579–588, 2012.
  13. Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Salakhutdinov and H. Larochelle, “Efficient learning of deep Boltzmann machines,” in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, pp. 693–700, May 2010.
  15. G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. G. E. Hinton, “Training products of experts by minimizing contrastive divergence,” Neural Computation, vol. 14, no. 8, pp. 1771–1800, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Á. Carreira-Perpiñán and G. E. Hinton, “On contrastive divergence learning,” in Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS '05), pp. 33–40, Citeseer, January 2005. View at Scopus
  18. G. E. Hinton and R. R. Salakhutdinov, “A better way to pretrain deep boltzmann machines,” in Advances in Neural Information Processing Systems 25, pp. 2447–2455, Curran Associates, 2012. View at Google Scholar
  19. R. Salakhutdinov and G. E. Hinton, “Deep Boltzmann machines,” in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AIS '09), pp. 448–455, April 2009.
  20. K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
  21. Y. Bengio, A. Courville, and P. Vincent, “Representation learning: a review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Collobert and J. Weston, “Fast semantic extraction using a novel neural network architecture,” in Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL '07), vol. 45, pp. 560–567, June 2007. View at Scopus
  23. J. Turian, L. Ratinov, and Y. Bengio, “Word representations: a simple and general method for semi-supervised learning,” in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 384–394, July 2010. View at Scopus
  24. E. H. Huang, R. Socher, C. D. Manning, and A. Y. Ng, “Improving word representations via global context and multipleword prototypes,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL '12), pp. 873–882, July 2012. View at Scopus
  25. T. Joachims, “A probabilistic analysis of the rocchio algorithm with tfidf for text categorization,” DTIC Document, 1996. View at Google Scholar
  26. L. van der Maaten, “Barnes-hut-sne,” http://arxiv.org/abs/1301.3342.