Abstract:
An information processing apparatus (10) includes: a formal language query accepting unit (12) that accepts a query expression and correct answer data; a semi-structured data accepting unit (14) that accepts semi-structured data that includes text nodes; a node text extraction unit (16) that extracts natural language text from the text node, as node text; a node text expression generation unit (18) that receives the node text from the a converter (100) and obtains node text expressions; an answer calculation unit (20) that calculates an answer to the query expression with use of the node text expressions; and an update unit (22) that, if the answer calculated by the answer calculation unit (20) matches the correct answer data, updates parameters in the converter (100) such that the corresponding node text expression is more likely to be output in the converter (100).
Abstract:
The present invention is capable of accurately and easily extracting a failure and a cause of occurrence of the failure on the basis of past cases. The present invention is provided with: a document storage unit (51) which stores a plurality of documents; a cause knowledge storage unit (52) which stores knowledge on cause containing expressions that represents a cause of acts and phenomena; a malfunction extraction unit (41) which extracts a malfunction expression from past cases represented by documents containing a question about a malfunction and an answer thereto; a possible cause extraction unit (42) which extracts, as an expression of a possible cause, an expression of a predetermined unit appearing in the past case from which the malfunction expression is extracted; a related document extraction unit (43) that extracts related documents regarding the expression of the possible cause and the malfunction expression, from the document storage unit; and a cause expression extraction unit (44) that selects a cause expression representing the cause of the malfunction from the expression of the possible cause, by using the related document and the knowledge on cause.
Abstract:
A processing device includes: an entity pre-processing means for receiving a set of atoms, the atom indicating a combination of a predicate and an associative array of entities that are arguments of the predicate, and calculating for each of the entities an entity feature vector for each of the entities, the vector reflecting a correspondence between keys and the entity in the associative array; and a post-processing means for receiving a query indicating contents of processing, and executing the processing indicated by the query using the entity feature vector.
Abstract:
The abductive inference device includes a hypothesis generation unit for creating a set of candidate hypotheses, observed information including a hypothesis from which a logical expression is derived based on a knowledge database including knowledge information expressing the logical expression for deriving a consequent from an antecedent, a conversion unit for calculating a constraint condition for the created set of candidate hypotheses and a weight for the constraint condition in accordance with predetermined processing; and a solver unit for calculating a hypothesis when a predetermined condition is satisfied based on the calculated constraint condition and the calculated weight.
Abstract:
A parameter learning apparatus 100 extracts 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.
Abstract:
A density estimation unit 81 is given observed covariates and estimates a conditional probability density of a random variable, denoting the real value that is the result of a smooth function map of the unobserved covariates, by training a regression model with the response corresponding to the random variable, and the regressors corresponding to the observed covariates. An integral estimation unit 82 that estimates the one-dimensional integral of the product of a sigmoidal function with the input random variable and the conditional probability density function of the random variable.
Abstract:
An inference system according to the present invention relates to inference from a starting state and a first rule set to an ending state. The inference system includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: receiving a parameter for use in selecting a second rule set from the first rule set; and visualizing the second rule set associated with the parameter.
Abstract:
In an information processing device, an observation data input means receives a pair of observation data and a predicted value of a target model for the observation data. A rule set input means receive a rule set including a plurality of rules, the rule including a pair of a condition and a predicted value corresponding to the condition. A satisfying rule selection means selects a satisfying rule from the rule set, the satisfying rule being a rule in which the condition becomes true for the observation data. An error calculation means calculates an error between a predicted value of the satisfying rule for the observation data and the predicted value of the target model. A surrogate rule determination means associates the rule which minimizes the error, among the satisfying rules, with the observation data as a surrogate rule for the target model.
Abstract:
The input unit 81 receives a set of rules each including a condition and a prediction, and pairs of observed data and correct answers. The stochastic decision list generator 82 assigns each rule in the set of rules to a plurality of positions in the decision list with a degree of occurrence indicating occurrence degree. The learning unit 83 updates a parameter determining the degree of occurrence so that a difference between an integrated prediction acquired by integrating, based on the degree of occurrence, the predictions of the rules whose conditions are satisfied by the observed data and the correct answer becomes small.
Abstract:
An information processing apparatus (10) includes a speech act formula generation unit (12) and an adjacency pair extraction unit (14). Dialogue text described in natural language is input to the speech act formula generation unit (12). The adjacency pair extraction unit (14) extracts, using preset pair information, pairs of speech act formulas that indicate adjacency pairs, from a plurality of speech act formulas generated by the speech act formula generation unit (12). Specifically, the adjacency pair extraction unit (14) extracts, as a pair of speech act formulas indicating an adjacency pair, a speech act formula generated from an arbitrary utterance text passage in the dialog text and one of a plurality of speech act formulas generated from a plurality of other utterance text passages, the one speech act formula including a predicate that forms a predicate pair with a predicate included in the arbitrary speech act formula.