Probabilistic resolution method for Japanese zero-subjects is described. It is designed to be used for the back-end processor of an automatic shortening system of long Japanese sentences in a Japanese to English machine translation system. Ordinary probabilistic resolution method uses (1) normal distribution model in the continuous probability space. In this article, we propose 3 new models. They are (2) quasi-normal distribution model in the continuous space,(3) 1st order log-linear distribution model in the discrete space and (4) 2nd order log-linear distribution model in the discrete space. For these four models, we make an experiment to measure the resolution accuracy. The test sample is from television broadcasting news. The measured accuracy by the cross validation test are 73%, 78%, 78% and 81% for (1), (2),(3) and (4) models, respectively. The unresolved examples show that semantic agreement between subject and predicate should be observed more accurately.

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