Nec Corporation

Kosuke Akimoto

Takuya Hiraoka

Kunihiko Sadamasa

Kunihiro Takeoka

Kong Aik Lee

Ryo Mizushima

Seng Pei Liew

At least one processor included in a question generation apparatus carries out: an acquiring process of acquiring a plurality of character strings and pieces of text; a question generating process of generating, for each of the plurality of character strings, a question an answer to which is that character string; an ambiguity judging process of judging, for each of a plurality of questions, whether there are a plurality of answers to that question; and an outputting process of outputting a question which has been judged to be the question to which there are a plurality of answers.

Question generation apparatus, question generation method, and recording medium for AI-assisted decision making

A feature calculation means calculates N features for first data and N features for second data by using N feature functions for obtaining a feature for data on the basis of the data. A similarity degree calculation means calculates a similarity degree between the first data and the second data on the basis of the N features for the first data and the N features for the second data. Values of N features obtained when the same data is substituted into the N feature functions are different from each other.

Similarity degree calculator, authorization system, similarity degree calculation method, similarity degree calculation program, and method for generating similarity degree calculation program

A parameter learning apparatusextracts one entity in a document and a related text representation as a one-term document fact, outputs a one-term partial predicate fact including only the one entity using a predicate fact that includes entities and a predicate, calculates a first one-term score indicating the degree of establishment of the one-term document fact using a one-term partial predicate feature vector, a one-term text representation feature vector, and a one-term entity feature vector that are calculated from parameters, calculates a second one-term score with respect to a combination of one entity and a predicate or a text representation that is not extracted as the one-term partial predicate fact, updates the parameters such that the first one-term score is higher than the second one-term score, and calculates a score indicating the degree of establishment of the predicate fact and a score indicating the degree of establishment of a combination of entities and a predicate that is not obtained as the predicate fact using these scores.

Parameter learning apparatus, parameter learning method, and computer readable recording medium

An entity combination and a text representation are obtained as a first fact, and the entity combination and a related predicate are obtained as a second fact. Word distributed representations are input to a neural network, real vectors at appearance positions of entities are specified and used as distributed representations. A first score indicating a degree of establishment of the first fact is calculated based on the distributed representations and on entity distributed representations. A second score indicating a degree of establishment is calculated with respect to an entity combination and a text representation that are not the first fact. A third score indicating a degree of establishment of the second fact is calculated based on predicate distributed representations and on entity distributed representations. A fourth score indicating a degree of establishment is calculated also with respect to an entity combination and a predicate that are not the second fact. The entity distributed representations, the predicate distributed representations, or weight parameters are updated by a gradient method, so that the first score becomes higher than one of the second score and the fourth score, and the third score becomes higher than one of the second score and the fourth score.

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Tokyo, Japan

Kosuke Akimoto