Abstract
Dependency structure analysis is one of the basic techniques in Japanese sentence analysis, and the Japanese dependency structure is usually represented by relationships between phrasal units called ‘bunsetsu.’ This analysis is a two-step procedure, and the first step is to prepare a dependency matrix in which each element represents how likely it is that one bunsetsu depends on another. The second step of the analysis finds an optimal set of dependencies for the entire sentence. In this paper we discuss a model used in the first step, a model for estimating dependency likelihood. There are two approaches to estimating the dependency likelihood: rule-based, and statistical.We take the statistical approach because electrically available corpora are getting large, and changing hand-crafted rules is costly. In our approach the value of each element in a dependency matrix is an estimated probability.A statistical model (here called the “old model”) that considers only the relationship between two bunsetsus when estimating those probabilities was earlier proposed. In this paper we propose a new model that estimates dependency likelihood by considering not only the relationship between two bunsetsus but also the relationship between the left bunsetsu and all of the bunsetsus to its right. Our implementation of this model is based on the ME (maximum entropy) model. When tested with the Kyoto University corpus the dependency accuracy obtained with this model was 88%, which is about 1% higher than that obtained with the old model even using exactly the same features.
