Abstract
In this paper, we present a deterministic dependency structure analyzer for Chinese. This analyzer implements two algorithms - Yamada and Nivre models -And two sorts of classifiers - Support Vector Machines and Maximum Entropy methods. We compare the performance of these 2x2 combinations. We evaluate the method on a dependency tagged corpus derived from the CKIP Treebank corpus. Then, we analyzed the errors in the experiments and found that some errors were caused by mistakes of nominal compounds analysis. Therefore we adopt an NP-chunker to solve this problem.
Cite
CITATION STYLE
Cheng, Y., Asahara, M., & Matsumoto, Y. (2005). Machine learning-based dependency analyzer for Chinese. In Proceedings of the International Conference on Chinese Computing 2005, ICCC 2005. Chinese and Oriental Languages Information Processing Society (COLIPS).
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