A smoothing n-gram language model addresses the limitations of the simple [[n-gram language model]] by assigning some of the density of probability distribution of frequent n-grams to rare or unseen n-grams. Strategies to overcome this sparsity include - [[discounting smoothing]]: redistribute probability density from high frequency to low frequency sequences. - [[additive smoothing]]: like discounting. - [[backoff smoothing]]: resort to shorter n-grams - [[interpolation smoothing]]: combine information from n-gram models of different lengths.