Abstract: A processing execution system, comprising at least one processor configured to: acquire second classification information relating to a classification of second data based on a first model which has learned a relationship between first data and first classification information relating to a classification of the first data; estimate, based on a predetermined estimation method, a validity relating to a combination of the second data and the second classification information; and execute predetermined processing based on an estimation result for the validity.

Abstract: An information processing device that generates a learning model includes a processor and a memory. The memory stores target data. The learning model is configured to output attribute information of a target based on a search query that has been input to search for the target. The processor is configured to execute a process that updates some of parameters included in the learning model by giving the target data to one or more training tasks and executing the one or more training tasks.

Abstract: Provided is a ranking determination system including at least one processor configured to: acquire a plurality of second classifications relating to second data based on a first model which has learned a relationship between first data and a first classification relating to the first data; acquire, for each of the second classifications, a second score relating to a validity of a combination of the second data and the second classification based on a second model which has learned a validity of a combination of third data and a third classification relating to the third data; and determine a second ranking for each of the plurality of second classifications based on the second score of each second classification.

Abstract: A causality recognizing apparatus includes a candidate vector generating unit configured to receive a causality candidate for generating a candidate vector representing a word sequence forming the candidate; a context vector generating unit generating a context vector representing a context in which noun-phrases of cause and effect parts of the causality candidate appear; a binary pattern vector generating unit, an answer vector generating unit and a related passage vector generating unit, generating a word vector representing background knowledge for determining whether or not there is causality between the noun-phrase included in the cause part and the noun-phrase included in the effect part; and a multicolumn convolutional neural network learned in advance to receive these word vectors and to determine whether or not the causality candidate has causality.

Abstract: An information processing device according to the present application includes an extraction unit and a subsequent stage generation unit. The extraction unit extracts a last conversation of a feedback utterance estimated to indicate a predetermined reaction of a second utterance subject relative to an utterance made by a first utterance subject, from a set of a plurality of conversations, based on a score assigned to the feedback utterance. The subsequent stage generation unit generates a subsequent stage classifier for deriving an index indicating a category of an unknown conversation, based on the last conversation extracted by the extraction unit.

Abstract: A causality recognizing apparatus includes a candidate vector generating unit configured to receive a causality candidate for generating a candidate vector representing a word sequence forming the candidate; a context vector generating unit generating a context vector representing a context in which noun-phrases of cause and effect parts of the causality candidate appear; a binary pattern vector generating unit, an answer vector generating unit and a related passage vector generating unit, generating a word vector representing background knowledge for determining whether or not there is causality between the noun-phrase included in the cause part and the noun-phrase included in the effect part; and a multicolumn convolutional neural network learned in advance to receive these word vectors and to determine whether or not the causality candidate has causality.

Abstract: A scenario passage pair recognizer includes: a text passage searching unit searching a set of text passages each including no more than a certain number of sentences of a document, and within which all noun phrases included in a scenario candidate co-occur; a feature extracting unit extracting a feature from each combination of the scenario candidate and each searched support passage; a classifier outputting a score indicating reliability of the scenario candidate based on the support passage as a source of the feature; and a score accumulating unit and a maximum value selecting unit, accumulating the scores output from the classifier and selecting the maximum value as the reliability of the scenario candidate. The scenario classifier determines plausibility of the scenario candidate as a causality based on the feature including the score output from the scenario passage pair recognizer.

Abstract: [Object] To provide a system for automatically and reliably collecting information belonging to a given category, and matching the information appropriately in a timely manner. [Solution] A text classifying device 30 analyzes dependency of collected texts by a morpheme analyzing unit 52 and a dependency analyzing unit 54. A problem report collecting unit 64 specifies a core consisting of noun+predicate in a text based on dependency relation of the text, and using a combination of noun classification (trouble/non-trouble) and predicate classification (excitatory/inhibitory), classifies the text to a problem report or the rest, by a method referred to as core-based matrix. Support information collecting device 66 and request message collecting device 68 collect support information and request messages in the similar manner.

Abstract: A credibility determining system enabling highly accurate credibility determination of given information includes causal relation knowledge DB 90 and search scope constraint DB 88. Causal relation knowledge each includes a combination of cause and result parts, and time and position constraints associated with the causal relation.

Abstract: [Object] An object is to provide a future scenario generating device capable of generating a huge number of appropriate future scenarios. [Solution] A future scenario generating device 272 includes: phrase pair DB 92 storing a large number of causality phrase pairs; a causality network building device 290 building a causality network by linking, of the phrases stored in causality phrase pair DB 92, phrases connectable as causality, using as a start point a phrase forming a main part of a question received by a question input unit 280; a community detecting device 294 detecting a community in the causality network; and a future scenario generating unit 298 generating a future scenario by linking phrases connectable as causality and belonging to the same community as the phrase as the start point until a predetermined end condition is satisfied.

Abstract: A question-answering system capable of appropriately answering a non-factoid question. The question-answering system responsive to a question, for taking out answer candidates from an object document storage unit; extracting plausible ones from the answer candidates; recognizing causal relation expression included in extracted answer candidates; determining whether or not a recognized causal relation is relevant as an answer; generating a feature vector related to a combination of the question and the searched answer candidate; and for calculating, when a feature vector is given, a score indicating a degree of how correct an answer candidate is as an answer to the question, for the question and the answer candidate as a source of the feature vector, and for outputting, as the answer to the question, an answer candidate having the highest score.

Abstract: [Object] An object of the present invention is to provide a system for collecting elements as a basis for generating a social scenario useful for people to make well-balanced good decision. [Solution] A scenario generating apparatus includes: a causality phrase pair DB 70 storing causality phrase pairs; synonimity generators 600, 602 and 604, searching, for each of the causality phrase pairs, causality phrase pairs having a cause phrase with causal consistency with the result phrase of the causality phrase pair, and generating join information for joining causality phrase pairs; join relation DB 610 storing the join information; and a causality joining unit 612 joining, to a result phrase of a causality phrase pair, a causality phrase pair having a cause phrase with causal consistency with the phrase by using the join information, and thereby linking causality. A join relation generator 606 that finds a hidden relation between phrases and linking causality phrase pairs may be provided.

Abstract: A system for collecting elements as a basis for generating a social scenario useful to make a well-balanced good decision. A complex predicate template collecting apparatus includes: a simple predicate template DB storing simple predicate templates having positive or negative polarity values added; an object noun dictionary; a complex predicate template candidate extractor extracting complex predicate template candidates each formed of a combination of a word specified by the object noun dictionary and a simple predicate template, from a WEB archive; a sahen-noun polarity adder, a quantifier noun polarity adder and a trouble-noun polarity adder, determining polarity of a noun in each of the complex predicate template candidate; and a complex predicate template polarity calculator calculating polarity of a complex predicate template candidate by a combination of the polarity of the noun and the polarity of the simple predicate template included in the complex predicate template.

Abstract: A scenario passage pair recognizer includes: a text passage searching unit searching a set of text passages each including no more than a certain number of sentences of a document, and within which all noun phrases included in a scenario candidate co-occur; a feature extracting unit extracting a feature from each combination of the scenario candidate and each searched support passage; a classifier outputting a score indicating reliability of the scenario candidate based on the support passage as a source of the feature; and a score accumulating unit and a maximum value selecting unit, accumulating the scores output from the classifier and selecting the maximum value as the reliability of the scenario candidate. The scenario classifier determines plausibility of the scenario candidate as a causality based on the feature including the score output from the scenario passage pair recognizer.

Abstract: An information processing device according to the present application includes an extraction unit and a subsequent stage generation unit. The extraction unit extracts a last conversation of a feedback utterance estimated to indicate a predetermined reaction of a second utterance subject relative to an utterance made by a first utterance subject, from a set of a plurality of conversations, based on a score assigned to the feedback utterance. The subsequent stage generation unit generates a subsequent stage classifier for deriving an index indicating a category of an unknown conversation, based on the last conversation extracted by the extraction unit.

Abstract: An annotation data generation assisting system includes: an input/output device receiving an input through an interactive process; morphological analysis system 380 and dependency parsing system performing morphological and dependency parsing on text data in text archive; first to fourth candidate generating units detecting a zero anaphor or a referring expression in the dependency relation of a predicate in a sequence of morphemes, identifying a position as an object of annotation and estimating candidates of expressions to be inserted by using language knowledge; a candidate DB storing estimated candidates; and an interactive annotation device reading candidates of annotation from candidate DB and annotate a candidate selected by an interactive process by input/output device.

Abstract: [Object] An object of the present invention is to provide a system for collecting elements as a basis for generating a social scenario useful to make well-balanced good decision. [Solution] A phrase pair collecting apparatus includes: a causality seed pair DB 410 storing seed pairs each consisting of a pair of phrases including a combination of a noun and a predicate template; a semantic relation pattern DB 400 storing semantic relation patterns between words; a word pair DB 402 storing word pairs related to any of the semantic relation patterns; a semantic relation pattern matching unit 470 for determining which of the semantic relation pattern matches a noun pair in each seed pair stored in causality seed pair DB 410; and a word pair replacing unit 472 for replacing the noun pair of a seed pair using, of the word pairs stored in the word pair DB 402, each of those word pairs which are related to the semantic relation pattern determined to be matching the noun pair, and thereby generating new hypotheses.

Abstract: A training device includes: a question issuing unit issuing a question stored in a question and expected answer storage unit to a question answering system; an answer candidate filtering unit, an answer candidate determining unit, training data generating/labeling unit, and a training data selecting unit, generating and adding to a training data storage unit training data for a ranking unit of question answering system, from pairs of a question and each of a plurality of answer candidates output with scores from why-question answering system; and an iteration control unit controlling question issuing unit, answer candidate filtering unit, answer candidate determining unit, training data generating/labeling unit and training data selecting unit such that training of the training device, issuance of question and addition of training data are repeated until an end condition is satisfied.

Abstract: annotation data generation assisting system includes: an input/output device receiving an input through an interactive process; morphological analysis system and dependency parsing system performing morphological and dependency parsing on text data in text archive; first to fourth candidate generating units detecting a zero anaphor or a referring expression in the dependency relation of a predicate in a sequence of morphemes, identifying a position as an object of annotation and estimating candidates of expressions to be inserted by using language knowledge; a candidate DB storing estimated candidates; and an interactive annotation device reading candidates of annotation from candidate DB and annotate a candidate selected by an interactive process by input/output device.

Abstract: A credibility determining system enabling highly accurate credibility determination of given information includes causal relation knowledge DB 90 and search scope constraint DB 88. Causal relation knowledge each includes a combination of cause and result parts, and time and position constraints associated with the causal relation.