Abstract: In order to classify, stably with high accuracy, a document to be classified, a document classification apparatus (1) includes: a strategy selection section (11) that selects at least one generation strategy from among a plurality of generation strategies for generating a hypothetical sentence related to a candidate classification as which a document is to be classified; a hypothetical sentence generation section (12) that generates, in accordance with the at least one generation strategy selected by the strategy selection section (11), the hypothetical sentence, which is a sentence related to the candidate classification; and a classification section (13) that determines, on the basis of entailment between the document and the hypothetical sentence, a classification as which the document is to be classified.

Abstract: In an information processing device, the class acquisition means acquires multiple classes to which target data is classified. The candidate set generation means generates a keyword candidate set which is a set of keyword candidates corresponding to each class. The keyword set generation means generates a keyword set corresponding to each class, from the keyword candidate set corresponding to each class, based on a relationship between the keywords included in the keyword candidate sets.

Abstract: The input means 181 accepts inputs of test data, a hierarchical structure in which a node of bottom layer represents a target class, and a classification score of a seen class as the classification score indicating a probability that the test data is classified into each class. The unseen class score calculation means 182 calculates the classification score of an unseen class based on uniformity of the classification score of each seen class. The matching score calculation means 183 calculates a matching score indicating similarity between the test data and each class label. The final classification score calculation means 184 calculates a final classification score indicating a probability that the test data is classified into the class so that the larger the classification score of each class, and the matching score, the larger the final classification score.

Abstract: The input means 181 accepts inputs of test data, a hierarchical structure in which a node of bottom layer represents a target class, and a classification score of a seen class as the classification score indicating a probability that the test data is classified into each class. The unseen class score calculation means 182 calculates the classification score of an unseen class based on uniformity of the classification score of each seen class. The matching score calculation means 183 calculates a matching score indicating similarity between the test data and each class label. The final classification score calculation means 184 calculates a final classification score indicating a probability that the test data is classified into the class so that the larger the classification score of each class, and the matching score, the larger the final classification score.

Abstract: A coffee beverage having, regardless of a liquid temperature of the beverage, reduced bitterness and little aftertaste is provided. The content of guaiacol and the content of furfuryl methyl sulfide in the beverage are adjusted to specific ranges.

Abstract: A coffee beverage having, regardless of a liquid temperature of the beverage, reduced bitterness and little aftertaste is provided. The content of guaiacol and the content of furfuryl methyl sulfide in the beverage are adjusted to specific ranges.