Japanese Dependency Structure Analysis Based on Support Vector Machines

@inproceedings{Kudo2000JapaneseDS,
  title={Japanese Dependency Structure Analysis Based on Support Vector Machines},
  author={Taku Kudo and Yuji Matsumoto},
  booktitle={EMNLP},
  year={2000}
}
This paper presents a method of Japanese dependency structure analysis based on Support Vector Machines (SVMs). Conventional parsing techniques based on Machine Learning framework, such as Decision Trees and Maximum Entropy Models, have difficulty in selecting useful features as well as finding appropriate combination of selected features. On the other hand, it is well-known that SVMs achieve high generalization performance even with input data of very high dimensional feature space… CONTINUE READING

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  • Experimental results on Kyoto University corpus show that our system achieves the accuracy of 89.09% even with small training data (7958 sentences).

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