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This paper reports the result of our experimental study on a new method of applying an association rule miner to discover useful information from customer inquiry database in a call center of a company. It has been claimed that association rule mining is not suited for text mining. To overcome this problem, we propose (1) to generate sequential data set of words with dependency structure from the Japanese text database, and (2) to employ a new method for extracting meaningful association rules by applying a new rule selection criterion. Each inquiry in the sequential data was represented as a list of word pairs, each of which consists of a verb and its dependent noun. The association rules were induced regarding each pair of words as an item. The rule selection criterion comes from our principle that we put heavier weights to co-occurrence of multiple items more than single item occurrence. We regarded a rule important if the existence of the items in the rule body significantly affects the occurrence of the item in the rule head. The selected rules were then categorized to form meaningful information classes. With this method, we succeeded in extracting useful information classes from the text database, which were not acquired by only simple keyword retrieval. Also, inquiries with multiple aspects were properly classified into corresponding multiple categories.