Abstract
Information technologies are being developed at unprecedented speed due to high performance and inexpensive computers and Internet have been widely available. The transmission and utilization of information become more diversified and borderless very rapidly. However, users may not make good use of huge amount of information by using conventional computers whose major functions are numerical calculation, symbol matching in information retrieval and deduction. Therefore, advanced utilization of contents of information is required gradually.
Learning and thinking are worth a while targets to such requirement and have been widely studied without useful results thus far. In order to realize machine learning and thinking, it is necessary to know meanings and characteristics of terms and various relationships among them, because technical terms are the most. convenient and powerful representation medium of abstract concepts. Therefore, the methods of constructing organized knowledge resources are based on extracting semantic relationships among terms. However, there are exceptional terms which may not be bypassed in natural languages. In this paper, we report the method of extracting hierarchical and associative relationships including such exceptions.
