Abstract
Background knowledge concerning to the input text is necessary when a computer tries to understand the text as well as syntactic and semantic information about it. This paper presents a method to construct an associative concept dictionary using large-scale association experiments. The dictionary includes semantic and contextual information about the stimulus words. In the association experiments, 100 stimulus words from the textbook of Japanese language used in elementary schools are given to subjects. They are requested to make association from the stimulus words about 7 tasks for each word. The tasks, for example, are higher level concepts, lower level concepts, actions, situations and so on. Conventional concept dictionaries have tree structures to express its hierarchical ones. Distances between concepts are calculated using number of links between the concepts. This paper shows a way to formulate the distance between concepts by using a linear programming method. Its parameters, especially frequency of the associated word and associated order of the word, are found significant for the distance calculation. By comparing the associative concept dictionary with EDR concept dictionary and WordNet using the distance information, it is found that the dictionary is more similar to WordNet than EDR.
