Pairwise classification and support vector machines
Advances in kernel methods
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Three generative, lexicalised models for statistical parsing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
A block-based robust dependency parser for unrestricted Chinese text
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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We present a method of dependency structure analysis for Chinese. The method is a variant of Yamada’s work (Yamada, 2003) originally proposed for English parsing. Our bottom-up parsing algorithm deterministically constructs a dependency structure for an input sentence. Support Vector Machines (SVMs) are utilized to determine the word dependency relations. Experimental evaluations on the CKIP Corpus show that the method is quite accurate on Chinese documents in several domains.