The formula of our model is the same as <cite>Ninomiya et al. (2006)</cite><cite>'s model 3</cite>. But, <cite>their model</cite> is not a probabilistic model with a reference distribution. Both our model and <cite>their model</cite> consist of the probabilities for lexical entries (= p model1 (T |w)) and the probabilities for phrase structures (= the rest of each formula). The only difference between our model and <cite>their model</cite> is the way of how to train model parameters for phrase structures. In both our model and <cite>their model</cite>, the parameters for lexical entries (= the parameters of p model1 (T |w)) are first estimated from the word and POS sequences independently of the parameters for phrase structures. That is, the estimated parameters for lexical entries are the same in both models, and hence the probabilities of p model1 (T |w) of both models are the same. Note that the parameters for lexical entries will never be updated after this estimation stage; i.e., the parameters for lexical entries are not estimated in the same time with the parameters for phrase structures. The difference of our model and <cite>their model</cite> is the estimation of parameters for phrase structures. In our model, given the probabilities for lexical entries, the parameters for phrase structures are estimated so as to maximize the entire probabilistic model (= the product of the probabilities for lexical entries and the probabilities for phrase structures) in the training corpus. In <cite>their model</cite>, the parameters for phrase structures are trained without using the probabilities for lexical entries, i.e., the parameters for phrase structures are estimated so as to maximize the probabilities for phrase structures only.