Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 401943, 10 pages
http://dx.doi.org/10.1155/2014/401943
Research Article

Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus

Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau

Received 30 August 2013; Accepted 8 December 2013; Published 19 March 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Ling Zhu 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. R. Koeling, “Chunking with maximum entropy models,” in Proceedings of the 2nd Workshop on Learning Language in Logic and the 4th Conference on Computational Natural Language Lerning, vol. 7, pp. 139–141, 2000.
  2. M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini, “Building a large annotated corpus of English: the Penn Treebank,” Computational Linguistics, vol. 19, no. 2, pp. 313–330, 1993. View at Google Scholar
  3. D. Yarowsky and G. Ngai, “Inducing multilingual POS taggers and NP bracketers via robust projection across aligned corpora,” in Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL '01), pp. 200–207, 2001.
  4. X. Zhu, Z. Ghahramani, and J. Lafferty, “Semi-supervised learning using Gaussian fields and harmonic functions,” in Proceedings of the 20th International Conference on Machine Learning (ICML '03), vol. 3, pp. 912–919, August 2003. View at Scopus
  5. Y. Altun, M. Belkin, and D. A. Mcallester, “Maximum margin semi-supervised learning for structured variables,” in Advances in Neural Information Processing Systems, pp. 33–40, 2005. View at Google Scholar
  6. A. Subramanya, S. Petrov, and F. Pereira, “Efficient graph-based semi-supervised learning of structured tagging models,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 167–176, October 2010. View at Scopus
  7. F. J. Och and H. Ney, “A systematic comparison of various statistical alignment models,” Computational Linguistics, vol. 29, no. 1, pp. 19–51, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Subramanya and J. Bilmes, “Semi-supervised learning with measure propagation,” The Journal of Machine Learning Research, vol. 12, pp. 3311–3370, 2011. View at Google Scholar · View at Scopus
  9. D. Das and N. A. Smith, “Semi-supervised frame-semantic parsing for unknown predicates,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 1435–1444, June 2011. View at Scopus
  10. S. Baluja, R. Seth, D. S. Sivakumar et al., “Video suggestion and discovery for you tube: taking random walks through the view graph,” in Proceedings of the 17th International Conference on World Wide Web, pp. 895–904, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Petrov, D. Das, and R. McDonald, “A universal part-of-speech tagset,” 2011, http://arxiv.org/abs/1104.2086.
  12. N. Xue, F. Xia, F.-D. Chiou, and M. Palmer, “The Penn Chinese TreeBank: phrase structure annotation of a large corpus,” Natural Language Engineering, vol. 11, no. 2, pp. 207–238, 2005. View at Publisher · View at Google Scholar · View at Scopus
  13. E. F. Tjong, K. Sang, and H. Déjean, “Introduction to the CoNLL- shared task: clause identification,” in Proceedings of the Workshop on Computational Natural Language Learning, vol. 7, p. 8, 2001.
  14. T. Berg-Kirkpatrick, A. Bouchard-Côté, J. de Nero, and D. Klein, “Painless unsupervised learning with features,” in Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pp. 582–590, Los Angeles, Calif, USA, June 2010. View at Scopus
  15. A. B. Goldberg and X. Zhu, “Seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization,” in Proceedings of the 1st Workshop on Graph Based Methods for Natural Language Processing, pp. 45–52, 2006.
  16. D. C. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical Programming B, vol. 45, no. 3, pp. 503–528, 1989. View at Google Scholar · View at Scopus
  17. L. Tian, F. Wong, and S. Chao, “An improvement of translation quality with adding key-words in parallel corpus,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '10), vol. 3, pp. 1273–1278, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. P. P. Talukdar and F. Pereira, “Experiments in graph-based semi-supervised learning methods for class-instance acquisition,” in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 1473–1481, July 2010. View at Scopus