We propose a neuron model to learn depth-rotation movement in three-dimensional space. The neuron model imitating neuron structure has a system resembling a neuron. We consider a neuron system, and expect to examine whether the system has reasonable function or not. Koch, Poggio and Torre (1982) believed that inhibition signal would shunt excitation signal on the dendrites, and signal functions as input and delay input. Thus, they were sure that function of directional selectivity is arisen by the delay system. Koch's conception is so important; therefore, we construct our neuron system based on their conception. We initialize the connections and the dendrites by random data, and train them by the back-propagation algorithm for three-dimensional movement. After learning, the neuron model for directional selectivity gets the ability of perceiving depth-rotation. It is similar to the real neuron's morphology.

Japanese has many noun phrase patterns of the type A no B consisting of two nouns A and B with an adnominal particle no. As the semantic relations between the two nouns in the noun phrase are not made explicit, the interpretation of the phrases depends mainly on the semantic characteristics of the nouns. This paper describes the semantic diversity of A no B and a method of semantic analysis for such phrases based on feature unification.

This paper proposes an analysis method for Japanese modality. In this purpose, meaning of Japanese modality is classified into four semantic categories and the role of it is formalized into five modality functions. Based on these formalizations, information and constraints to be applied to the modality analysis procedure are specified. Then by combining these investigations with case analysis, the analysis method is proposed. This analysis method has been applied to Japanese analysis for machine translation.