Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Deterministic dependency parsing of English text
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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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).