Chinese Deterministic Dependency Analysis with Consideration of Long-Distance Dependency

H. Zhou, D. Huang, and Y. Yang (PRC)


Chinese dependency analysis, Nivre’s algorithm, Support Vector Machines, Preference Learning, and root node finder


According to Chinese syntax, implement a deterministic Chinese dependency analyzer based on an improved Nivre’s algorithm which considers long-distance dependency. It is difficult to parse long-distance dependency with conventional deterministic dependency analysis methods. The proposed method parses a sentence deterministically without ignoring long-distance dependency. In addition, we also construct a root node finder to divide the sentence into two sub-sentences. Support Vector Machines are applied to identify Chinese dependency structure. We compare the performance of two sorts of classifiers – Support Vector Machines and Preference Learning in root node finding. Experiments using the Harbin University of Technology Corpus show that the method outperforms previous system by 6.46% accuracy. The dependency accuracy achieves 79.44% even with small training data (4000 sentences).

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