Smoothing for an n-gram language model is an algorithm that can assign a non-zero probability to an unseen n-gram. Smoothing is an essential technique for an n-gram language model due to the data sparseness problem. However, in some circumstances it assigns an improper amount of probability to unseen n-grams. In this paper, we present a novel method that adjusts the improperly assigned probabilities of unseen n-grams by taking advantage of the agglutinative characteristics of Korean language. In Korean, the grammatically proper class of a morpheme can be predicted by knowing the previous morpheme. By using this characteristic, we try to prevent grammatically improper n-grams from achieving relatively higher probability and to assign more probability mass to proper n-grams. Experimental results show that the proposed method can achieve 8.6% - 12.5% perplexity reductions for Katz backoff algorithm and 4.9% - 7.0% perplexity reductions for Kneser-Ney Smoothing.