Japanese dependency structure analysis based on maximum entropy models
Kiyotaka Uchimoto, Satoshi Sekine, Hitoshi Isahara · 1999
This paper describes a dependency structure analysis of Japanese sentences based on the maximum entropy models. Our model is created by learning the weights of some features from a training corpus to predict the dependency between bunsetsus or phrasal units. The dependency accuracy of our system is 87.2% using the Kyoto University corpus. We discuss the contribution of each feature set and the relationship between the number of training data and the accuracy.