Nec Corporation

Nec Solution Innovators, Ltd.

Dai Kusui

Hironori Mizuguchi

Yukitaka Kusumura

Masaaki Tsuchida

Kenji Tateishi

Yusuke Muraoka

Itaru Hosomi

Hideki Kawai

Toshiyuki Kamiya

Haruka Saito

Izumi Nagisa

A learning assistance apparatusincludes: a data acquisition unitthat acquires a problem and an answer corresponding thereto from a data structure that includes the problem and the answer as data; an operation reception unitthat 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 unitthat 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.

Learning assistance apparatus, learning assistance method, and computer-readable storage medium

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.

Prediction function creation device, prediction function creation method, and computer-readable storage medium

The information processing deviceprocesses document collections having tags permitting semantic class identification appended to each document and comprises a search unit, 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 unitwhich, 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 unitidentifies any of the semantic class units based on the first frequency and the second frequency of the matching documents.

Information processing device, information processing method, and computer-readable recording medium

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.

Data structure, index creation device, data search device, index creation method, data search method, and computer-readable recording medium

A boundary word identification unit () identifies a boundary word belonging to a plurality of categories among words gathered in dictionary growth processing. Then, a category membership degree calculation unit () 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 (). Next, a category update unit () 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 () and updates information stored in a gathered-by-category word memory unit () so that the determination result is reflected.

Dictionary creation device, word gathering method and recording medium

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. The present invention is provided with: a document data addition unit () which inputs a document which includes one or more words; a level setting unit () which sets a number of topics at each level in accordance with a hierarchical structure of topics for hierarchically estimating latent topics of a word; a higher-level constraint creation unit () which, on the basis of results of topic estimation at a given level with regard to a word within the document, creates a higher-level constraint indicating an identifier of a topic for which there is a possibility of being assigned to the word and a probability of being assigned to the topic; and a higher-level-constraint-attached topic estimation unit () which, when estimating the probability of each word being assigned to each topic, refers to the higher-order constraint, uses the probability of being assigned to a parent topic at the higher level as a weight, and performs estimation processing to a lower-level topic.

Word latent topic estimation device and word latent topic estimation method

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.

Data classifier system, data classifier method and data classifier program

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. The priority calculation means calculates priority in displaying classification axis candidates, the number of which is reduced by the classification axis candidate reduction means.

Data classifier system, data classifier method and data classifier program stored on storage medium

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 () that classifies a set of sample documents based on missing patterns of a first feature vector, a first learning unit () 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 () 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 () 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.

Document evaluation apparatus, document evaluation method, and computer-readable recording medium using missing patterns

A communication assistance device () includes a communication level determination unit () so as to determine a level of a relationship between users who communicate with each other. The communication level determination unit () 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.

Communication assistance device, communication assistance method, and computer readable recording medium

The disclosed apparatus uses a training data generation apparatus, which generates training data used for creating characteristic expression extraction rules. The training data generation apparatusincludes: a training data candidate clustering unit, 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 unitwhich, 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.

Training data generation apparatus, characteristic expression extraction system, training data generation method, and computer-readable storage medium

A communication assistance device () includes a communication level determination unit () and a topic recommendation unit () 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 () 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 () 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.

An information classification device () is provided with an union of sets determination unit () which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit () 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. Each individual determination rule is created for respective person using the aforementioned result of correct/incorrect determination and the feature amount.

Information classification device, information classification method, and computer readable recording medium

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.

Meaning extraction system, meaning extraction method, and recording medium

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 of the link.

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Tokyo, Japan

Dai Kusui