Deep belief nets for natural language call-routing

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 5680-5683

  Sarikaya, Ruhi ^a   Hinton, Geoffrey E ^b   Ramabhadran, Bhuvana ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Call Routing; DBN; Deep Learning; RBM

Indexed keywords

CALL-ROUTING; CLASSIFICATION ACCURACY; DBN; DEEP LEARNING; EFFICIENT LEARNING; GENERATIVE MODEL; MAXIMUM ENTROPY; NATURAL LANGUAGE CALL ROUTING; NATURAL LANGUAGES; RBM; SPEECH CLASSIFICATION; TEXT CLASSIFICATION; UNLABELED DATA;

NATURAL LANGUAGE PROCESSING SYSTEMS; SIGNAL PROCESSING; SPEECH COMMUNICATION; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

NEURAL NETWORKS;

EID: 80051625051     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5947649     Document Type: Conference Paper

References (16)

1. G. E. Hinton, A Practical Guide Training Restricted Boltzmann Machines, University of Toronto Machine Learning Technical Report, UTML TR 2010-003.
2. G. E. Hinton, Training products of experts by minimizing contrastive divergence, Neural Computation, vol. 14, pp. 1771-1800, 2002.
3. D. Erhan, Y. Bengio, A. Courville, P. Manzagol and P. Vincent, Why Does Unsupervised Pre-training Help Deep Learning?, Journal of Machine Learning Research, vol. 11, pp. 625-660, 2010.
4. R. E. Schapire and Y. Singer, Boostexter: A boosting based system for text categorization, Machine Learning, vol. 39, no. 2/3, pp. 135-168, 2000.
5. G. E. Hinton, and R. Salakhutdinov,"Reducing the dimensionality of data with neural networks," Science, vol. 313. no. 5786, 2006, pp. 504-507.
6. A. Y. Ng and M. I. Jordan, On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes, Advances in Neural Information Processing Systems, vol. 11, 2002.
7. G. E. Hinton, S. Osindero and Y. W. Teh, A Fast Learning Algorithm for Deep Belief Nets, Advances in Neural Computation, vol. 18, no. 7, pp. 1527-1554, 2006.
8. G. E. Hinton,"Learning multiple layers of representation," TRENDS in Cognitive Sciences, vol. 11. no. 10, pp. 428-434. 2007.
9. M. Welling, M. Rosen-Zvi and G. E. Hinton,"Exponential fanily harmoniums with an application to information retrieval," In Advances in Neural Information Processing Systems, pp. 1481-1488. Cambridge, MA. MIT Press. 2005.
10. G. E. Hinton, "Training product of experts by minimizing constrastive divergence," Neural Computation, vol. 14. no. 18, 2002, pp. 1527-1554.
11. G. E. Dahl, M. Ranzato, A. Momamed and G. E. Hinton, "Phone Recognition with the Mean-Covariance Restricted Boltzmann Machines," Advances in Neural Information Processing Systems NIPS, 2010.
12. R. R. Salakhutdinov and G.E. Hinton, "Replicated softmax: An undirected topic model," Advances in Neural Information Processing Systems, vol. 22, 2007.
13. V. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, NY, USA, 1995.
14. S. Chen and R. Rosenfeld, "A survey smoothing techniques for ME models", IEEE Trans. SAP, 8(1):37-50, 2001.
15. S. D. Pietra, V. D. Pietra and J. Lafferty, "Inducing features of random fields", IEEE Trans. Pattern. Analysis Mach. Int., 19(4):380-93, 1997.
16. R. Sarikaya, H-K. J. Kuo V. Goel, and Y. Gao, "Exploiting Unlabeled Data Using Multiple Classifiers for Improved Natural Language Call-Routing", In Proc. Interspeech, Lisbon Portugal, September 2005.