Abstract
To realize computers understanding natural language needs an association-system which outputs words strongly related to input words. This study aims to construct a concept-base which is a main element of the association-system. In the concept-base, the meaning of a word is defined by a set of an attribute expressing the feature of a word and the weight representing the importance to the word. In our study, we model concepts as a chain of words defined by the concept-base. The first concept-base automatically constructed contains not a few unsuitable attributes and, therefore, the reliability of weights is also questionable. Making the automatically constructed concept-base a starting point, we are aiming to achieve a new refining method based on the reliability of attributes so that noises will be removed and more appropriate weight will be gained. Moreover, this paper shows effects of the proposed method by presenting an evaluation by human senses and an experiment that utilizes the degree of association in test data.
