A maximum entropy modeling method is provided which is capable of selecting valid feature functions by excluding invalid feature functions, reducing a modeling time and realizing a high accuracy. The maximum entropy modeling method includes: a first step (S1) of setting an initial value for a current model; a second step (S2) of setting a set of feature functions as a candidate set; a third step (S3) of comparing observed probabilities of respective feature functions included in the candidate set with estimated probabilities of the feature functions according to a current model, and determining the feature functions to be excluded from the candidate set; a fourth step (S4) of adding the remaining feature functions included in the candidate set after excluding the feature functions to be excluded to the respective sets of feature functions of the current model, and calculating parameters of a maximum entropy model thereby to create a plurality of new approximate models; and a fifth step (S5) of calculating a likelihood of learning data using the approximate models, and replacing the current model with a model that is determined based on the likelihood of learning data.
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