Apply a Rough Set-Based Classifier to Dependency Parsing

Author:

Ji Yangsheng,Shang Lin,Dai Xinyu,Ma Ruoce

Publisher

Springer Berlin Heidelberg

Reference20 articles.

1. Nivre, J., Hall, J., Nilsson, J.: Memory-Based Dependency Parsing. In: Ng, H.T., Riloff, E. (eds.) Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL), Boston, Massachusetts, 6-7 May 2004, pp. 49–56 (2004)

2. Yuret, D.: Dependency Parsing as a Classification Problem. In: Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL) (2006)

3. Ji, Y.S., Shang, L.: RoughTree A Classifier with Naive-Bayes and Rough Sets Hybrid in Decision Tree Representation. In: 2007 IEEE International Conference on Granular Computing, Silicon, Valley, USA, pp. 221–227 (2007)

4. Kononenko, I.: Comparison of inductive and Naive Bayesian learning approaches to automatic knowledge acquisition. In: Wielinga, B., et al. (eds.) Current Trends in Knowledge Acquisition, IOS Press, Amsterdam (1990)

5. Domingos, P., Pazzani, M.: Beyond independence: Conditions for the optimality of the simple Bayesian classifier. In: Proc. 13th Intl. Conf. on Machine Learning, pp. 105–112. Morgan Kaufmann, San Francisco (1996)

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