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keywords: common sense judgement, unknown word processing, concept base, degree of association, neural network SummaryWhen we humans receive uncertain information, we interpret it properly, so we can expand the conversation, and take the proper actions. This is possible because we have "common sense" concerning the basic word concept, which is built up from long time experience storing knowledge of our language. Of the common sense we use in our every day lives we think that there are; common sense concerning quantity such as size, weight, speed, time, or place; common sense concerning sense or feeling such as hot, beautiful, or loud; and moreover common sense concerning emotion such as happy or sad. In order to make computers closer to human beings, we think that the construction of a "Common Sense Judgment System" which deals with these kinds of common sense is necessary.When aiming to realize this "Common Sense Judgment System" and trying to make a computer have the same common sense knowledge and judgment ability as human beings, a very important factor is the handling of unknown words. Judgment concerning words which were given to the computer as knowledge before hand, it can refer to that knowledge, and the process will have no problem at all. But when an unknown word, which is not registered as knowledge, is inputted, how to process that word is a very difficult problem.In this paper, by using a concept base, which is made from several electric dictionaries; the degree of association, which is done based on the concept base; neural network, putting the closeness of meaning in consideration, we propose a method of unknown word processing, which connects an inputted unknown word to a representing word that is registered in the judgment knowledge base, and we will verify its effectiveness by experiment applied to the emotional judgment subsystem.

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We think that realizing computers understanding natural language needs an association-system which outputs words related to input words.The associationsystem consists of the concept-base and an algorithm measuring the degree of association between words.In the concept-base,the meaning of a word is defined by a set of words(attributes)expressing the semantic feature of the word,with their weights show importance of the attributes to the word.This study aims to construct the concept-base.The first concept-base is automatically made from several electronic dictionaries.There are,however,many unsuitable attributes in the concept-base, and their weights are unreliable because of the automatic construction.A refining method to delete the unsuitable attributes and to correct the unreliable weighs by the degree of attribute reliability has proposed.But the refining method cannot increase the attributes.This paper proposes the method expanding the attributes of the refined concept-base,by logical relations between the words,and shows effects of the proposed method by evaluation with test data and the degree of association.

keywords: information retrieval, information ranking, degree of association, concept-base SummaryIn this information-oriented society that we live in, a method of retrieving necessary information is needed. Obtaining necessary information, by information retrieval using the Boolean method, which uses keywords given by the user, narrows corresponding documents down to the thousands or ten thousands, which results in the user not knowing which document to look at first. Therefore, by transferring the user's retrieval query and documents into quantitative values and ranking the requested information in order of relation to the user's demand, it is needed to give the user information that fits the user's demand. As a calculation method to transfer the retrieval query and the documents into quantitative values, the vector space model exists, but this paper shows that the method of ranking information, using the concept-base and the degree of association, is effective.