Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 485737, 8 pages
http://dx.doi.org/10.1155/2014/485737
Research Article

Quantum Neural Network Based Machine Translator for Hindi to English

1Department of Computer Science & Engineering, Thapar University, Patiala, Punjab 147 004, India
2Department of Mathematics, National Institute of Technology Rourkela, Odisha 769 008, India

Received 31 August 2013; Accepted 8 January 2014; Published 27 February 2014

Academic Editors: P. Bala and J. Silc

Copyright © 2014 Ravi Narayan 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. L. Rodríguez, I. García-Varea, and A. José Gámez, “On the application of different evolutionary algorithms to the alignment problem in statistical machine translation,” Neurocomputing, vol. 71, pp. 755–765, 2008. View at Google Scholar
  2. S. Ananthakrishnan, R. Prasad, D. Stallard, and P. Natarajan, “Batch-mode semi-supervised active learning for statistical machine translation,” Computer Speech & Language, vol. 27, pp. 397–406, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Ortiz-Martínezb, I. García-Vareaa, and F. Casacubertab, “The scaling problem in the pattern recognition approach to machine translation,” Pattern Recognition Letters, vol. 29, pp. 1145–1153, 2008. View at Google Scholar
  4. J. Andrés-Ferrer, D. Ortiz-Martínez, I. García-Varea, and F. Casacuberta, “On the use of different loss functions in statistical pattern recognition applied to machine translation,” Pattern Recognition Letters, vol. 29, no. 8, pp. 1072–1081, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Khalilov and J. A. R. Fonollosa, “Syntax-based reordering for statistical machine translation,” Computer Speech & Language, vol. 25, no. 4, pp. 761–788, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. R. M. K. Sinha, “An engineering perspective of machine translation: anglabharti-II and anubharti-II architectures,” in Proceedings of the International Symposium on Machine Translation, NLP and Translation Support System (ISTRANS '04), pp. 134–138, Tata McGraw-Hill, New Delhi, India, 2004.
  7. R. M. K. Sinha and A. Thakur, “Machine translation of bi-lingual Hindi-English (Hinglish),” in Proceedings of the 10th Machine Translation Summit, pp. 149–156, Phuket, Thailand, 2005.
  8. R. Ananthakrishnan, M. Kavitha, J. H. Jayprasad et al., “MaTra: a practical approach to fully-automatic indicative English-Hindi machine translation,” in Proceedings of the Symposium on Modeling and Shallow Parsing of Indian Languages (MSPIL '06), 2006.
  9. S. Raman and N. R. K. Reddy, “A transputer-based parallel machine translation system for Indian languages,” Microprocessors and Microsystems, vol. 20, no. 6, pp. 373–383, 1997. View at Google Scholar · View at Scopus
  10. A. Chandola and A. Mahalanobis, “Ordered rules for full sentence translation: a neural network realization and a case study for Hindi and English,” Pattern Recognition, vol. 27, no. 4, pp. 515–521, 1994. View at Publisher · View at Google Scholar · View at Scopus
  11. N. B. Karayiannis and G. Purushothaman, “Fuzzy pattern classification using feed-forward neural networks with multilevel hidden neurons,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1577–1582, Orlando, Fla, USA, June 1994. View at Scopus
  12. G. Purushothaman and N. B. Karayiannis, “Quantum Neural Networks (QNN's): inherently fuzzy feedforward neural networks,” IEEE Transactions on Neural Networks, vol. 8, no. 3, pp. 679–693, 1997. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Kretzschmar, R. Bueler, N. B. Karayiannis, and F. Eggimann, “Quantum neural networks versus conventional feedforward neural networks: an experimental study,” in Proceedings of the 10th IEEE Workshop on Neural Netwoks for Signal Processing (NNSP '00), pp. 328–337, December 2000. View at Scopus
  14. Z. Daqi and W. Rushi, “A multi-layer quantum neural networks recognition system for handwritten digital recognition,” in Proceedings of the 3rd International Conference on Natural Computation (ICNC '07), pp. 718–722, Hainan, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Brants, A. C. Popat, P. Xu, F. J. Och, and J. Dean, “Large language models in machine translation,” in Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 858–867, Prague, Czech Republic, June 2007. View at Scopus
  16. K. Papineni, S. Roukos, T. Ward, and W. -J. Zhu, “BLEU: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL '02), pp. 311–318, Philadelphia, Pa, USA, 2002.
  17. G. Doddington, “Automatic evaluation of machine translation quality using n-gram co-occurrence statistics,” in Proceedings of the 2nd International Conference on Human Language Technology Research, 2002.
  18. C. Y. Lin, “Rouge: a package for automatic evaluation of summaries,” in Proceedings of the Workshop on Text Summarization Branches Out, Barcelona, Spain, 2004.
  19. S. Banerjee and A. Lavie, “METEOR: an automatic metric for MT evaluation with improved correlation with human judgments,” in Proceedings of the Workshop on Intrinsic and Extrinsic Evaluation Measures for MT, Association of Computational Linguistics (ACL), Ann Arbor, Mich, USA, 2005.