Abstract: A learning assistance apparatus 10 includes: a data acquisition unit 11 that acquires a problem and an answer corresponding thereto from a data structure that includes the problem and the answer as data; an operation reception unit 12 that receives a first operation of moving a display region in a first direction, a second operation of moving the display region in a second direction, and a third operation of moving the display region in a third direction; and a display control unit 13 that causes another problem to be displayed on a screen according to a movement when the first operation is received after the problem is displayed, causes the answer corresponding to the displayed problem to be displayed on the screen when the second operation is received after the problem is displayed, and causes another problem to be displayed on the screen when the third operation is received after the answer is displayed due to the second operation.

Abstract: The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

Abstract: A learning assistance apparatus 10 includes a video presentation unit 11 that presents a first video of a first person performing a specific agricultural task and a second video of a second person performing a specific agricultural task, an inquiry processing unit 12 that presents, to a user, questions regarding disparities between the agricultural task of the first person in the first video and the agricultural task performed by the second person in the second video and receives a response by the user, and an educational material suggestion unit 13 that specifies an educational material for describing the specific agricultural task based on a result of the response and suggests the specified educational material.

Abstract: An explicit knowledge conversion assistance apparatus 100 includes: a table acquisition unit 10 that acquires a table in which, for each agricultural task for a specific crop, an influence degree indicating influence that the agricultural task has on an outcome, a type of skill required for the agricultural task, and a level of a worker required for the agricultural task are defined by numerical values; an evaluation value calculation unit 20 that, for each agricultural task, calculates an evaluation value based on the values for the influence degree, the type of skill, and the level, using the table; and a priority rank setting unit 30 that sets a priority rank for each agricultural task based on the calculated evaluation value.

Abstract: A learning assistance apparatus 10 includes: a data acquisition unit 11 that acquires a problem and an answer corresponding thereto from a data structure that includes the problem and the answer as data; an operation reception unit 12 that receives a first operation of moving a display region in a first direction, a second operation of moving the display region in a second direction, and a third operation of moving the display region in a third direction; and a display control unit 13 that causes another problem to be displayed on a screen according to a movement when the first operation is received after the problem is displayed, causes the answer corresponding to the displayed problem to be displayed on the screen when the second operation is received after the problem is displayed, and causes another problem to be displayed on the screen when the third operation is received after the answer is displayed due to the second operation.

Abstract: A skill transfer facilitating apparatus 10 is an apparatus for facilitating skill transfer, and includes a data accumulation unit 11 that accumulates data regarding tasks that are executed using skills that are to be transferred; and a rule creation unit 12 that extracts, from the data that is accumulated, task names, task execution results, and task reasons as information, executes, for each combination of a task name and a task execution result thus extracted, statistical processing with respect to each corresponding task reason, and then creates, for each task, a rule that serves as a condition for executing the task or a condition for not executing the task, based on the result of statistical processing.

Abstract: The information processing device 1 processes document collections having tags permitting semantic class identification appended to each document and comprises a search unit 2, which creates multiple semantic class units containing one, two, or more semantic classes based on a taxonomy that identifies relationships between semantic classes, and a frequency calculation unit 3 which, for each of the semantic class units, identifies documents that match that semantic class unit in the document collections and, for these matching documents, calculates a first frequency that represents the frequency of occurrence in a designated document collection and a second frequency that represents the frequency of occurrence in non-designated document collections. Once the calculations have been performed, the search unit 2 identifies any of the semantic class units based on the first frequency and the second frequency of the matching documents.

Abstract: A boundary word identification unit (103) identifies a boundary word belonging to a plurality of categories among words gathered in dictionary growth processing. Then, a category membership degree calculation unit (104) calculates, for each category to which the boundary word belongs, a category membership degree indicating a degree to which the boundary word belongs to the category on the basis of information recorded in a gathering process memory unit (108). Next, a category update unit (105) determines the category to which the boundary word belongs on the basis of the category membership degree calculated by the category membership degree calculation unit (104) and updates information stored in a gathered-by-category word memory unit (109) so that the determination result is reflected.

Abstract: In an inverted list of each node in a taxonomy, among each node, an inverted list of the highest node is a list of integer values indicating an identifier of search subject data, and an inverted list of a node other than the highest node, in place of the identifier, is a list of integer values indicating a position in an inverted list corresponding to a node that is higher by one than the node. Furthermore, a list of integer values in an inverted list of each node is divided into two or more blocks, and a differential value between an integer value and an integer value directly before the integer value in the block is converted into a bit string of a variable length integer code.

Abstract: Provided are a word latent topic estimation device and a word latent topic estimation method which are capable of hierarchically performing processing and which are capable of rapidly estimating latent topics of a word while taking into consideration a mixed state of topics.

Abstract: A data classifier system of the present invention selects a plurality of classifications correlated to data groups so as to output classification axes based on hierarchical classifications and data groups. The data classifier system includes a basic category accumulation means, a classification axis candidate creation means and a priority calculation means. The basic category accumulation means accumulates classifications serving as basic categories used for selecting desired classifications in advance. The classification axis candidate creation means creates classification axis candidates based on combinations of classifications each correlated to at least one data among descendant classifications of each basic category. The priority calculation means calculates priorities with respect to the classification axis candidates created by the classification axis candidate creation means based on hierarchical distances of classifications in the classified hierarchy.

Abstract: A data classifier system of the present invention selects a plurality of classifications correlated to data groups so as to output classification axes based on hierarchical classifications and data groups. The data classifier system includes a basic category accumulation means, a classification axis candidate reduction means and a priority calculation means. The basic category accumulation means accumulates classifications serving as basic categories used for desired classifications in advance. The classification axis candidate reduction means selects a plurality of classifications from among classifications descendant from each basic category so as to create classification axis candidates, thus reducing classification axis candidates subjected to calculations based on data quantity of classifications and hierarchical distances of classifications.

Abstract: The prediction function creation device according to the present invention for creating a prediction function to derive an objective variable by using a set of samples that include explanatory variables and an objective variable, the device includes: a clustering unit that clusters the respective samples by giving labels, and assigns weights to each label in accordance with patterns of missing values for the explanatory variables in labeled samples; a child model creation unit that makes portions of the training data partial training data on the basis of the weights, and determines an explanatory variable that constitutes the prediction function on the basis of patterns of missing values for the explanatory variables in the samples; and a mixture model creation unit that creates the prediction function with respect to each pattern of missing values by using the explanatory variable and the determined partial training data.

Abstract: In order to accurately learn a function for evaluating documents, even in the case where sample documents having missing feature values are included as training data, a document evaluation apparatus is provided with a data classification unit (3) that classifies a set of sample documents based on missing patterns of a first feature vector, a first learning unit (4) that uses feature values that are not missing in the first feature vector and evaluation values to learn a first function for calculating a first score which is a weighted evaluation value for each classification, a feature vector generation unit (5) that computes a feature value corresponding to each classification using the first score, and generates a second feature vector having the computed feature values, and a second learning unit (6) that uses the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating documents targeted for evaluation.

Abstract: A communication assistance device (10) includes a communication level determination unit (11) so as to determine a level of a relationship between users who communicate with each other. The communication level determination unit (11) determines the level (communication level) of the relationship between the users based on similarity between the users obtained from preference information showing preferences of the users, and on user action records showing records of actions taken by a certain user toward a partner user with whom the certain user communicates out of the users.

Abstract: The disclosed apparatus uses a training data generation apparatus 2, which generates training data used for creating characteristic expression extraction rules. The training data generation apparatus 2 includes: a training data candidate clustering unit 21, which clusters a plurality of training data candidates assigned labels indicating annotation classes based on feature values containing respective context information, and a training data generation unit 22 which, by referring to each cluster obtained using the clustering results, obtains the distribution of the labels of the training data candidates within the cluster, identifies training data candidates that meet a preset condition based on the obtained distribution, and generates training data using the identified training data candidates.

Abstract: An information classification device (1) is provided with an union of sets determination unit (10) which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit (11) which applies a plurality of individual determination rules to the content to be classified which has been determined as correct, determines whether the content matches the condition, and performs correct/incorrect determination again regarding the content to be classified which has been determined as correct on the basis of the determination result of each individual determination rule. The union of sets determination rule is created using a result of correct/incorrect determination previously performed by two or more people with respect to a plurality of contents which are different from the contents to be classified, and also using feature amounts of respective different contents.

Abstract: A communication assistance device (10) includes a communication level determination unit (11) and a topic recommendation unit (16) so as to determine a level of a relationship between users who communicate with each other and provide communication assistance using the result of the determination. The communication level determination unit (11) determines the level (communication level) of the relationship between the users based on similarity between the users obtained from preference information showing preferences of the users, and on user action records showing records of actions taken by a certain user toward a partner user with whom the certain user communicates out of the users. The topic recommendation unit (16) selects, from among a group of topics prepared in advance, a topic that can be transmitted to the partner user based on the determined level of the relationship between the users and on preferences of the certain user and the partner user.

Abstract: A meaning extraction device includes a clustering unit, an extraction rule generation unit and an extraction rule application unit. The clustering unit acquires feature vectors that transform numerical features representing the features of words having specific meanings and the surrounding words into elements, and clusters the acquired feature vectors into a plurality of clusters on the basis of the degree of similarity between feature vectors. The extraction rule generation unit performs machine learning based on the feature vectors within a cluster for each cluster, and generates extraction rules to extract words having specific meanings. The extraction rule application unit receives feature vectors generated from the words in documents which are subject to meaning extraction, specifies the optimum extraction rules for the feature vectors, and extracts the meanings of the words on the basis of which the feature vectors were generated by applying the specified extraction rules to the feature vectors.

Abstract: A linkage information output apparatus includes: a linkage information retrieval unit for acquiring, upon receiving source information, destination information linked with the source information, a frequency of occurrence of the source information, a frequency of occurrence of linked each of the destination information, and a frequency of occurrence of a link of the source information and each of the destination information from a linkage information accumulation unit; a recognition degree calculation unit calculating, based on each acquired frequency of occurrence, a recognition degree of the source information, a recognition degree of each acquired destination information, and a recognition degree of each link; and a high interest information narrowing unit selecting destination information to output from among each destination information based on a combination of two or more among a recognition degree of the source information, a recognition degree of the destination information, and a recognition degree