In this paper, we propose a new information theoretic method for competitive learning, and demonstrate that it can discover some linguistic rules in unsupervised ways more explicitly than the traditional competitive method. The new method can directly control competitive unit activation patterns to which input-competitive connections are adjusted. This direct control of the activation patterns permits considerable flexibility for connections, and shows the ability to detect salient features not captured by the traditional competitive method. We applied the new method to a linguistic rule acquisition problem. In this problem, unsupervised methods are needed because children learn rules without any explicit instruction. Our results confirmed that the new method can give similar results as those by the traditional competitive method when input data are appropriately coded. However, we could see that when unnecessary information is given to a network, the new method can filter it out, while the performance of the traditional method is degraded by unnecessary information. Because data in actual cognitive and engineering problems usually contain redundant and unnecessary information, the new method has good potential for discovering regularity in actual problems.