A proposal of 3-dimensional self-organizing memory and its application to knowledge extraction from natural language

In this paper, we propose a 3-dimensional self-organizing memory and describe its application to knowledge extraction from natural language. First, the proposed system extracts a relation between words by JUMAN (morpheme analysis system) and KNP (syntax analysis system), and stores it in short-term memory. In the short-term memory, the relations are attenuated with the passage of processing. However, the relations with high frequency of appearance are stored in the long-term memory without attenuation. The relations in the long-term memory are placed to the proposed 3-dimensional self-organizing memory. We used a new learning algorithm called "Potential Firing" in the learning phase. In the recall phase, the proposed system recalls relational knowledge from the learned knowledge based on the input sentence. We used a new recall algorithm called "Waterfall Recall" in the recall phase. We added a function to respond to questions in natural language with "yes/no" in order to confirm the validity of proposed system by evaluating the quantity of correct answers.