Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014 (2014), Article ID 959328, 7 pages
http://dx.doi.org/10.1155/2014/959328
Research Article

Chinese Unknown Word Recognition for PCFG-LA Parsing

NLP2CT Laboratory, Department of Computer and Information Science, University of Macau, Macau

Received 30 August 2013; Accepted 10 March 2014; Published 9 April 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Qiuping Huang 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. D. Klein and C. D. Manning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1, pp. 423–430, 2003.
  2. E. Charniak and M. Johnson, “Coarse-to-fine n-best parsing and MaxEnt discriminative reranking,” in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL '05), pp. 173–180, June 2005. View at Scopus
  3. S. Petrov and D. Klein, “Improved inference for unlexicalized parsing,” in The Conference of the North American Chapter of the Association for Computational Linguistics, pp. 404–411, April 2007. View at Scopus
  4. Z. Huang and M. Harper, “Self-training PCFG grammars with latent annotations across languages,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 832–841, August 2009. View at Scopus
  5. Z. Huang, M. Harper, and W. Wang, “Mandarin part-of-speech tagging and discriminative reranking,” in Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL '07), pp. 1093–1102, June 2007. View at Scopus
  6. P. P. Talukdar and K. Crammer, “New regularized algorithms for transductive learning,” in Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 442–457. View at Publisher · View at Google Scholar
  7. X. Zeng, D. F. Wong, L. S. Chao, and I. Trancoso, “Graph-based semi-supervised model for joint Chinese word segmentation and part-of-speech tagging,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), pp. 770–779, Association for Computational Linguistics, Sofia, Bulgaria.
  8. L. Zhu, D. F. Wong, and L. S. Chao, “Unsupervised chunking based on graph propagation from bilingual corpus,” The Scientific World Journal, vol. 2014, Article ID 401943, 2014. View at Publisher · View at Google Scholar
  9. X. Zeng, D. F. Wong, L. S. Chao, I. Trancoso, L. He, and Q. Huang, “Lexicon expansion for latent variable grammars,” Pattern Recognition Letters, vol. 42, pp. 47–55, 2014. View at Publisher · View at Google Scholar
  10. S. Petrov, L. Barrett, R. Thibaux, and D. Klein, “Learning accurate, compact, and interpretable tree annotation,” in Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL '06), pp. 433–440, July 2006. View at Scopus
  11. M. Attia, J. Foster, D. Hogan, J. L. Roux, L. Tounsi, and J. V. Genabith, “Handling unknown words in statistical latent-variable parsing models for Arabic, English and French,” in Proceedings of the NAACL HLT 1st Workshop on Statistical Parsing of Morphologically-Rich Languages, pp. 67–75, 2010.
  12. Q. Zhou, “Annotation scheme for Chinese treebank,” Journal of Chinese Information Processing, vol. 18, no. 4, pp. 1–8, 2004. View at Google Scholar
  13. O. Chapelle, B. Scholkopf, and A. Zien, Semi-Supervised Learning, vol. 2, MIT Press, Cambridge, UK, 2006.
  14. M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: a geometric framework for learning from labeled and unlabeled examples,” The Journal of Machine Learning Research, vol. 7, pp. 2399–2434, 2006. View at Google Scholar · View at Scopus
  15. X. Zhu, J. Lafferty, and Z. Ghahramani, “Combining active learning and semi-supervised learning using gaussian fields and harmonic functions,” in Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining (ICML '03), pp. 58–65, 2003.
  16. A. Subramanya and J. Bilmes, “Soft-supervised learning for text classification,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1090–1099, October 2008. View at Scopus
  17. P. P. Talukdar and K. Crammer, “New regularized algorithms for transductive learning,” in Machine Learning and Knowledge Discovery in Database, pp. 442–457, 2009.
  18. D. Das and N. A. Smith, “Graph-based lexicon expansion with sparsity-inducing penalties,” in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 677–687, 2012.
  19. S. Baluja, R. Seth, D. 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
  20. D. Das and S. Petrov, “Unsupervised part-of-speech tagging with bilingual graph-based projections,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 600–609, June 2011. View at Scopus
  21. 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
  22. Y. Bengio, O. Delalleau, and N. L. Roux, Label Propagation and Quadratic Criterion, MIT Press, 2006.
  23. 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), pp. 912–919, Washington, DC, USA, 2003.
  24. C. Zhu, R. H. Byrd, P. Lu, and J. Nocedal, “L-BFGS-B: fortran subroutines for large scale bound constrained optimization,” ACM Transactions on Mathematical Software, vol. 23, pp. 550–560, 1997. View at Publisher · View at Google Scholar
  25. M. Collins, “Head-driven Statistical Models for Natural Language Parsing,” Computational Linguistics, vol. 29, no. 4, pp. 589–637, 2003. View at Publisher · View at Google Scholar
  26. M. Harper and Z. Huang, “Chinese statistical parsing,” in Handbook of Natural Language Processing and Machine Translation, J. Olive, C. Christianson, and J. McCary, Eds., Springer, 2011. View at Google Scholar
  27. S. Sekine and M. Collins, “Evalb,” 1997, http://nlp.cs.nyu.edu/evalb.