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 column meaning candidate selection means 303 selects a candidate for meaning of a column whose meaning is to be inferred. A column similarity computation means 304 computes, for each candidate for meaning selected by the column meaning candidate selection means 303, a score indicating a similarity between the selected candidate for meaning and meaning of each column other than the column whose meaning is to be inferred contained in a table. A column meaning identification means 305 identifies meaning of the column whose meaning is to be inferred from the candidates for meaning of the column with use of the score computed by the column similarity computation means 304.

Abstract: A learning system includes: an acquisition unit that obtains document data; a generation unit that generates a key phrase from the document data; a restoration unit that restores the document data from the generated key phrase; and a learning unit that learns parameters of the generation unit on the basis of the document data and the restored document data. According to such a learning system, high-precision learning can be performed even when there is no key phrase as a correct answer.

Abstract: In order to expand a taxonomy more accurately, an information processing apparatus (1) includes: an obtaining section (11) that obtains a taxonomy which indicates a relationship between a plurality of phrases; a generating section (12) that generates a new phrase which differs from each of the plurality of phrases, with reference to a first sentence group; and an expanding section (13) that expands the taxonomy by associating the new phrase with any of the plurality of phrases. One or both of the generating section (12) and the expanding section (13) use a language model.

Abstract: Provided is an information provision system that can provide workers with useful information for combining tables, so that even workers with little specialized knowledge can smoothly proceed with the task of combining multiple tables. An input unit 81 receives input of multiple tables. An identification unit 82 identifies a pair of columns that are in a combinable relationship, identifies that a pair of tables to which the individual columns forming the pair belong is the pair of tables to be combined, and identifies a combine method of the tables to be combined. An output unit 83 outputs the pair of tables to be combined, the pair of columns in a combinable relationship, and the combine method of the tables to be combined.

Abstract: A data index unit 80 manages data of source tables. A data searching unit 90 searches the joinable source tables for the query table. A group indexing unit 81 divides each the source table into a collection of sets, and generates, for each divided set, a group of similar cells included in the set using a similarity function for calculating cell similarity. A data filtering unit 91 searches the source table including a set in which the number of pairs of a first cell included in a divided query table set and a second cell in a group including cells similar to the first cell exceeds a predetermined threshold.

Abstract: An input unit 81 receives input of response data with a response attached to input data by each worker. A learning unit 82 learns a worker model which is a model that predicts a response to new input data using the input response data, for each worker. The input unit 81 receives input of both response data of first response data in which a label included in output candidate label data indicating a candidate label to be assigned to the input data is assigned to the input data, and second response data in which a label not included in the output candidate label data is assigned to the input data, and the learning unit 82 learns the worker model using the both response data of the first response data and the second response data.

Abstract: The acquisition unit 52A acquires request information S1, which is information regarding possession data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53A determines whether or not there is shortage data other than the possession data required to perform a designation task that is designated. The notification unit 54A notifies, when the determination unit 53A determines that there is the shortage data, the owner terminal 2 of shortage data information S2 that is information regarding shortage information.

Abstract: The acquisition unit 52B acquires sales request information S1, which is request information regarding a sale of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53B determines whether or not there is a user who has a past record of performing a task or who is expected to perform the task, the task being subjected to a positive effect when the data is used for the task. The notification unit 54B notifies, in a case where the determination unit 53B determines that there is a user who has a past record of performing the task or who is expected to perform the task, the owner terminal 2 of candidate buyer information S2 indicating information regarding the user.

Abstract: The acquisition unit 52B acquires sales request information S1, which is request information regarding a sale of data owned by a data owner, from an owner terminal 2 used by the data owner. The determination unit 53B determines, on a basis of the sales request information S1, whether or not there is a customer who demands the data. The notification unit 54B notifies, in a case where the determination unit 53B determines that there is the customer, the owner terminal 2 of sales response information S2 indicating information regarding the customer for the data.

Abstract: Provided is an information provision system that can provide workers with useful information for combining tables, so that even workers with little specialized knowledge can smoothly proceed with the task of combining multiple tables. An input unit 81 receives input of multiple tables. An identification unit 82 identifies a pair of columns that are in a combinable relationship, identifies that a pair of tables to which the individual columns forming the pair belong is the pair of tables to be combined, and identifies a combine method of the tables to be combined. An output unit 83 outputs the pair of tables to be combined, the pair of columns in a combinable relationship, and the combine method of the tables to be combined.

Abstract: An input unit 81 inputs an annotation result that is data to which a label is added based on an annotator's answer, and label addition information that indicates an inter-label structure. An answer integration unit 82 integrates the annotation results and estimates the label of the data. A skill estimation unit 83 estimates a skill of the annotator based on a difference between the estimated label and the labels included in the annotation results. An update unit 84 updates, based on the estimated skill of the annotator, the feature of a task for adding a label the inter-label structure of which is specified based on the label addition information to the data, the update being performed so that the feature conforms to the annotation results. An output unit 85 outputs the label estimated by the answer integration unit 82. The answer integration unit 82 estimates the label based on a weight calculated in accordance with closeness of the skill of the annotator and the feature of the task to the label.

Abstract: A column meaning candidate selection means 303 selects a candidate for meaning of a column whose meaning is to be inferred. A column similarity computation means 304 computes, for each candidate for meaning selected by the column meaning candidate selection means 303, a score indicating a similarity between the selected candidate for meaning and meaning of each column other than the column whose meaning is to be inferred contained in a table. A column meaning identification means 305 identifies meaning of the column whose meaning is to be inferred from the candidates for meaning of the column with use of the score computed by the column similarity computation means 304.