The nature of statistical learning theory
The nature of statistical learning theory
Japanese dependency structure analysis based on maximum entropy models
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
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
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In Japanese, long sentences often have parallel relations. It is difficult to recognize the parallel relation since conventional Japanese dependency parsers do not parse parallel relation especially. This paper proposes a new Japanese dependency parser based on parallel relation. The proposed model first parses parallel relation if there is a parallel key in a sentence. Then parse the other segments based on parallel relation. Experiments using the Kyoto University Corpus show that the system achieves the accuracy of 90.03% even with small training data.