7. The document evaluation method according to claim 4 in which, in the step (c), the feature values constituting the second feature vector are generated by normalizing the first scores so as to fall within a set range.
4. A document evaluation method for evaluating a document using a set of sample documents having a first feature vector consisting of a plurality of feature values and evaluation values of the documents, comprising the steps of: (a) classifying the set of sample documents, based on a missing pattern indicating a set of indices whose feature values are missing in the first feature vector; (b) using feature values that are not missing in the first feature vector and the evaluation values to learn, for each classification, a first function for calculating a first score which is a weighted evaluation value for each classification; (c) computing a feature value corresponding to each classification using the first score, and generating a second feature vector having the computed feature values; (d) using the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating a document targeted for evaluation; and (e) measuring an appearance frequency of the sample documents for each missing pattern, based on the set of sample documents, and matching a missing pattern whose appearance frequency is less than or equal to a set threshold to a missing pattern that is most similar to said missing pattern and whose appearance frequency is greater than the threshold.