Classification system, method, and program

A classification method performed by a computer and comprising:

• accepting input of test data that is document data to be classified, a hierarchical structure in which a node of a bottom layer represents a target class, and a classification score of each of a plurality of seen classes for the document data, wherein the classification score of each seen class indicates a probability that the test data is correctly classified into the each seen class;
• calculating a classification score of each of a plurality of unseen classes for the document data based on uniformity of the classification score of each seen class;
• allocating the classification scores of the seen classes under a parent node of the unseen classes to the classification scores of the unseen class such that a sum of the classification scores of the seen classes and the unseen classes under the parent node are equal to the classification scores of the parent node;
• for each class of the seen classes and the unseen classes, calculating a matching score indicating similarity between the test data and a class label of the each class, by applying the class label of each class and the test data to a matcher which inputs the class label indicating linguistic meaning of the each class and a document sample and outputs the matching score corresponding to a similarity between the class label and the document sample;
• and
• calculating a final classification score indicating a probability that the test data is classified into a class selected from the seen classes and the unseen classes such that the larger the classification score of the selected class and the larger the matching score for the selected class are, the larger the final classification score is.

A non-transitory computer readable information recording medium storing a classification program executable by a processor to perform a method comprising:

• accepting input of test data that is document data to be classified, a hierarchical structure in which a node of a bottom layer represents a target class, and a classification score of each of a plurality of seen classes for the document data, wherein the classification score of each seen class indicates a probability that the test data is correctly classified into the each seen class;
• calculating a classification score of each of a plurality of unseen classes for the document data based on uniformity of the classification score of each seen class;
• allocating the classification scores of the seen classes under a parent node of the unseen classes to the classification scores of the unseen class such that a sum of the classification scores of the seen classes and the unseen classes under the parent node are equal to the classification scores of the parent node;
• for each class of the seen classes and the unseen classes, calculating a matching score indicating similarity between the test data and a class label of the each class, by applying the class label of each class and the test data to a matcher which inputs the class label indicating linguistic meaning of the each class and a document sample and outputs the matching score corresponding to a similarity between the class label and the document sample;
• and
• calculating a final classification score indicating a probability that the test data is classified into a class selected from the seen classes and the unseen classes such that the larger the classification score of the selected class and the larger the matching score for the selected class are, the larger the final classification score is.