1. A model learning method comprising: first updating, by using a computer, a first score for a feature based on a weight of training sample, the features being a cue to extract a correct structure from the training sample corresponding to the weight, to calculate a model defined by the first score; performing a process, by using a computer, the process comprising: identifying a maximum score from second scores, each of the second scores is assigned, by the model, to candidate structures other than the correct structure among a plurality of candidate structures derived from the training sample; and first calculating a difference between the maximum score and a second score that is assigned, by the model, to the correct structure; second calculating a confidence degree of the model based on an upper limit value of error defined by the difference; and second updating, by using a computer, the weight based on the confidence degree and the difference.