Abstract: A CPU of a training data creation apparatus includes an extraction unit, a derivation unit, and a creation unit. The extraction unit extracts a plurality of unique expressions that are specific term phrases from a medical document. The derivation unit derives a degree of association indicating a degree of association among the plurality of unique expressions. The creation unit selects a target term phrase that is a term phrase as a target to be masked.

Abstract: Provided are an information processing method, an information processing apparatus, and a program that realize generation of a dataset of a user behavior history of different domains. An information processing method includes acquiring a dataset in one domain, the dataset being a dataset in which the response variable, the explanatory variable, and a plurality of variables excluding the response variable and the explanatory variable are applied, selecting a plurality of domain candidate variables that are domain candidates from the plurality of variables excluding the response variable and the explanatory variable, generating a dataset candidate that divides the dataset by using the domain candidate variables, and generating, in a case where each of the dataset candidates is a dataset in a different domain, a divided dataset by setting the domain candidate variables as domains.

Abstract: Provided are an information processing method, an information processing apparatus, and a program capable of generating data of a user behavior history of different domains. An information processing method executed by one or more processors, the method includes: causing the one or more processors to represent a simultaneous probability distribution between a response variable and an explanatory variable, with a behavior for an item of an user as the response variable, for a dataset including a behavior history with respect to a plurality of the items of a plurality of the users, modify a part of the simultaneous probability distribution, and generate data based on the modified simultaneous probability distribution.

Abstract: A prediction device that predicts information related to a patient based on medical data of the patient, including: a processor; and a memory connected the processor, in which the processor being configured to execute: data set extraction processing of extracting M data sets by classifying pieces of medical data of plural patients into any one of M types of predetermined attributes, training data set generation processing of generating M training data sets related to the M types of attributes from the M data sets, similarity calculation processing of calculating, for each pair of the M types of attributes, a similarity between the attributes, training processing of training one or plural machine learning models based on the similarity between the attributes by using the M training data sets, and prediction processing of causing the one or plural machine learning models to predict the information related to the patient.

Abstract: There is provided a device for generating a data merging rule for a machine learning model, the device including: a processor, and a memory connected to or built in the processor, in which the processor is configured to execute specifying processing of specifying a combination of feature vectors that are included in a data set including a correct answer label and are allowed to be merged, and rule generation processing of generating a merging rule of the feature vectors based on a combination of the feature vectors that are allowed to be merged.

Abstract: There is provided a device for determining an execution order of a plurality of tasks, the device including: processor; and a memory connected to or built in the processor, in which the processor is configured to execute execution order determination processing of determining an execution order of the plurality of tasks under a condition that an output of at least one task of the tasks is input to the task in a subsequent stage.

Abstract: An information processing apparatus includes at least one processor, in which the processor is configured to: for a machine learning model that uses a document data group including a plurality of document data as input and outputs output data, derive an evaluation value in the machine learning model for each document data included in the document data group.

Abstract: An information processing apparatus including: plural models including at least a first model that uses first data as input to carry out a first prediction task and a second model that uses second data as input to carry out a second prediction task different from the first prediction task, and at least one processor, in which the processor is configured to: acquire information data representing information related to a specific target; select a selection model to be used for prediction from among the plural models based on the information data; and derive a degree of importance to the specific target for each item included in the information data by using the selection model.

Abstract: A model generation apparatus includes a processor that is configured to: acquire information data for learning, acquire document data for learning, extract a first portion and a second portion having a lower rate of match with the information data than the first portion from the document data based on a rate of match between the information data and each portion of the document data, generate a first machine learning model by using first learning data in which first data for learning included in the information data is used as input data and the first portion is used as correct answer data, and generate a second machine learning model by using second learning data in which second data for learning included in the information data is used as input data and the second portion is used as correct answer data.

Abstract: There is provided an information processing method, an information processing apparatus, and a program capable of preparing a high performance model for an unknown introduction destination facility even in a case where a domain of the introduction destination facility is unknown at a step of training a model. The information processing method includes: dividing a dataset, which indicates a behavior of a user on an item for each of combinations of a plurality of the users and a plurality of the items, into a plurality of subgroups that are robust with respect to domain shift; generating a local model for performing a prediction of the behavior of the user on the item for each of the subgroups; and combining a plurality of the local models generated for each of the subgroups.

Abstract: Provided are an information processing method, an information processing system, and a program capable of generating a suggested item list that is robust against the domain shift by applying a plurality of models that are trained by using datasets of domains different from an introduction destination domain. The information processing system is configured to: acquire one or more candidate items from each of a plurality of models trained by using datasets in one or more domains different from an introduction destination domain; and select, from among a plurality of the acquired candidate items, a plurality of candidate items having different domains from each other as suggested items and generate a suggested item list that is a suggested item list including a plurality of the suggested items and that has robust performance against a domain shift.

Abstract: An information processing system includes one or more processors, in which the one or more processors are configured to: perform a training, by using data of each facility collected at each of a plurality of facilities, of a local model that predicts, for each of the facilities, a behavior of a user on an item; perform an evaluation of a difference between models in a parameter of the local model trained for each of the facilities; and correct the training of the local model such that the difference between the models is small based on a result of the evaluation.

Abstract: An information processing method executed by one or more processors, in which a plurality of models, which are trained by using one or more of datasets including a behavior history of a user on an item, which is collected at each of a plurality of first facilities different from each other, are prepared, and the one or more processors include acquiring a characteristic of a second facility, which is different from the plurality of first facilities and acquiring a characteristic of each of the plurality of first facilities, evaluating a similarity degree between the acquired characteristic of the second facility and the characteristic of the first facility where the dataset, which is used for the training of the model, is collected, and selecting a model suitable for the second facility from among the plurality of models based on the similarity degree.

Abstract: There is provided an information processing method, an information processing apparatus, and a program capable of preparing a high performance model for an unknown introduction destination facility even in a case where a domain of the introduction destination facility is unknown at a step of training a model. An information processing method executed by one or more processors, in which the one or more processors include representing characteristics of a plurality of second facilities different from a first facility where a dataset, which is used for a training of a model that predicts a behavior of a user on an item, is collected, and training a plurality of the models such that prediction performance at each of the second facilities is improved according to the characteristics of each of the second facilities.

Abstract: An information processing apparatus includes a processor configured to input an arbitrary text and plural answer candidates extracted from the text to a question generator that generates a question sentence associated with an answer, cause the question generator to generate a question sentence associated with the text and the plural answer candidates, and cause the question generator to output generated data including a set of the plural answer candidates and the question sentence.

Abstract: A CPU of a training data creation apparatus includes an extraction unit, a derivation unit, and a creation unit. The extraction unit extracts a plurality of unique expressions that are specific term phrases from a medical document. The derivation unit derives a degree of association indicating a degree of association among the plurality of unique expressions. The creation unit selects a target term phrase that is a term phrase as a target to be masked.

Abstract: An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: derive a reliability degree of a trained teacher model based on training data used for training the teacher model and predetermined sample data; derive an output target based on output data obtained by inputting the sample data to the teacher model and the reliability degree; and train a student model such that output data obtained by inputting the sample data to the student model approaches the output target.

Abstract: An information processing apparatus comprising at least one processor, wherein the at least one processor is configured to: acquire a plurality of pieces of element information related to an image; generate a plan which defines a description order of elements corresponding to the plurality of pieces of element information in a sentence in which the elements are described; and generate the sentence based on the plan.

Abstract: An answer generating device includes: an input that receives an input of analysis target data which is data to be questioned and analyzed; a processor; and an output. The processor being configured to, upon input of the question and the analysis target data and by execution of a program, extract answers to the question from the analysis target data using plural single answer models prepared in advance, the answers each being extracted independently for each of the plural single answer models, calculate a frequency of appearance of each of tokens in the extracted answers, and extract a single answer or multiple answers based on the frequency of appearance, and output the single answer or multiple answers to the output.

Abstract: An information processing apparatus includes: a processor configured to: search first action histories for second action histories similar to a specific action history of a second user to whom a recommendation is to be provided, in which each first action history represents objects which a first user corresponding to the first action history took a specific action for and each of which a degree of preference of the corresponding first user is given, and the specific action history represents objects which the second user took the specific action for; select an object to be recommended to the second user based on the degrees of preference for the respective objects which are given to the respective second action histories; and present the selected object.