Abstract: A text classifier 90 for answer identification is capable of highly accurate identification of an answer candidate to a question, by effectively using background knowledge related to the question, in order to extract an answer candidate to the question, the text classifier including: a BERT (Bidirectional Encoder Representation from Transformers) receiving a question and an answer candidate as inputs; a knowledge integration transformer receiving the output of BERT as an input; a background knowledge representation generator receiving a question and an answer as inputs and generating a group of background knowledge representation vectors for the question; and a vector converter respectively converting the question and the answer candidate to embedded vectors and inputting the same to the background knowledge representation generator.

Abstract: A program for training a representation generator generating a representation representing an answer part included in a passage to classify whether the passage is related to an answer or not. The program causes a computer to operate as: a fake representation generator responsive to a question and a passage for outputting a fake representation representing an answer part of the passage; a real representation generator for outputting, for the question and a core answer, a real representation representing the core answer, in the same format as fake representation; a discriminator for discriminating whether fake representation and real representation are a real or fake representation; and a generative adversarial network unit training the discriminator and fake representation generator through generative adversarial network such that error determination of fake representation is maximized and error determination of real representation is minimized.

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 question answering device includes: a general word vector converter converting a question and an answer to semantic vectors in accordance with general context; a general sentence level CNN 214, in response to similarities of semantic vectors between words in question and answer and to strength of causality between the words, for weighting each semantic vector to calculate sentence level representations of the question and the answer; a general passage level CNN 218, in response to similarity between sentence level representations of question and answer, and to strength of relation of vectors in the sentence level representations viewed from causality, for weighting the sentence level representation to calculate a passage level representation for the question and answer passage; and a classifier determining whether or not an answer is a correct answer, based on the similarities between outputs from CNNs 214 and 218.

Abstract: A memory for a question-answering device that reduces influence of noise on answer generation and is capable of generating highly accurate answers includes: a memory configured to normalize vector expressions of answers included in a set of answers extracted from a prescribed background knowledge source for each of a plurality of mutually different questions and to store the results as normalized vectors; and a key-value memory access unit responsive to application of a question vector derived from a question for accessing the memory and for updating the question vector by using a degree of relatedness between the question vector and the plurality of questions and using the normalized vectors corresponding to respective ones of the plurality of questions.

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 summary generating apparatus includes a text storage device storing text with information indicating a portion to be focused on; word vector converters vectorizing each word of the text and adding an element indicating whether the word is focused on or not to the vector and thereby converting the text to a word vector sequence; an LSTM implemented by a neural network performing sequence-to-sequence type conversion, pre-trained by machine learning to output, in response to each of the word vectors of the word vector sequence input in a prescribed order, a summary of the text consisting of the words represented by the word sequence; and input units inputting each of the word vectors of the word vector sequence in the prescribed order to the neural network.

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 summary generating apparatus includes; a text storage device storing text with information indicating a portion to be focused on; word vector converters vectorizing each word of the text and adding an element indicating whether the word is focused on or not to the vector and thereby converting the text to a word vector sequence; an LSTM implemented by a neural network performing sequence-to-sequence type conversion, pre-trained by machine learning to output, in response to each of the word vectors of the word vector sequence input in a prescribed order, a summary of the text consisting of the words represented by the word sequence; and input units inputting each of the word vectors of the word vector sequence in the prescribed order to the neural network.

Abstract: A question answering device includes: a general word vector converter converting a question and an answer to semantic vectors in accordance with general context; a general sentence level CNN 214, in response to similarities of semantic vectors between words in question and answer and to strength of causality between the words, for weighting each semantic vector to calculate sentence level representations of the question and the answer; a general passage level CNN 218, in response to similarity between sentence level representations of question and answer, and to strength of relation of vectors in the sentence level representations viewed from causality, for weighting the sentence level representation to calculate a passage level representation for the question and answer passage; and a classifier determining whether or not an answer is a correct answer, based on the similarities between outputs from CNNs 214 and 218.

Abstract: A question-answering system includes a storage unit storing expressions representing causality; an answer receiving unit receiving a question and answer passages each including an answer candidate to the question; a causality expression extracting unit extracting a causality expression from each of the answer passages; a relevant causality expression extracting unit selecting, for a combination of the question and an answer passage, an expression most relevant to the combination, from the storage unit; and a neural network receiving the question, the answer passages, semantic relation expressions related to the answer passages, and one of the relevant expressions for the combination of the question and the answer passages, and selecting an answer to the question from the answer passages.

Abstract: [Object] To provide a device assisting a user to easily generate, in relation to an issue of interest to the user, a question sentence guaranteed to have an answer of a certain accuracy or higher in a question-answering system. [Solution] A question sentence generating device is used with a question-answering system, and it includes: word receiving means for receiving a word 480 as a source for generating a question sentence; and question sentence generating database 502 comprised of a plurality of entries for generating a question sentence. Each of the plurality of entries has a word as a key and includes an answer sentence pattern co-occurring with the word, used in the question-answering system.

Abstract: An entailment pattern pair extension apparatus 50 extends entailment pairs by generating an n-term entailment pair from an m-term entailment pair, where m and n are integers not smaller than 0 and satisfying m<n. Each entailment pair includes a first language pattern and a second language pattern entailed by the first language pattern. Entailment pattern pair extension apparatus 50 includes: a generation rule storage unit 110 storing generation rules for generating n-term entailment pairs from m-term entailment pairs, and a binary pair adding unit 112 receiving m-term entailment pair, determining, for each generation rule stored in the generation rule storage unit 110, whether its condition is satisfied by the m-term pair, and if the condition is satisfied, applying a modification rule of the generation rule to each language pattern constituting the m-term entailment pair.

Abstract: A context analysis apparatus includes an analysis control unit for detecting a predicate of which subject is omitted and antecedent candidates thereof, and an anaphora/ellipsis analysis unit determining a word to be identified. The anaphora/ellipsis analysis unit includes: word vector generating units generating a plurality of different types of word vectors from sentences for the antecedent candidates; a convolutional neural network receiving as an input a word vector and trained to output a score indicating the probability of each antecedent candidate being the omitted word; and a list storage unit and a identification unit determining a antecedent candidate having the highest score. The word vectors include a plurality of word vectors each extracted at least by using the object of analysis and character sequences of the entire sentences other than the candidates. Similar processing is also possible on other words such as a referring expression.

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: An entailment pattern pair extension apparatus 50 extends entailment pairs by generating an n-term entailment pair from an m-term entailment pair, where m and n are integers not smaller than 0 and satisfying m<n. Each entailment pair includes a first language pattern and a second language pattern entailed by the first language pattern. Entailment pattern pair extension apparatus 50 includes: a generation rule storage unit 110 storing generation rules for generating n-term entailment pairs from m-term entailment pairs, and a binary pair adding unit 112 receiving m-term entailment pair, determining, for each generation rule stored in the generation rule storage unit 110, whether its condition is satisfied by the m-term pair, and if the condition is satisfied, applying a modification rule of the generation rule to each language pattern constituting the m-term entailment pair.

Abstract: [Object] To provide a device assisting a user to easily generate, in relation to an issue of interest to the user, a question sentence guaranteed to have an answer of a certain accuracy or higher in a question-answering system. [Solution] A question sentence generating device is used with a question-answering system, and it includes: word receiving means for receiving a word 480 as a source for generating a question sentence; and question sentence generating database 502 comprised of a plurality of entries for generating a question sentence. Each of the plurality of entries has a word as a key and includes an answer sentence pattern co-occurring with the word, used in the question-answering system.

Abstract: [Object] An object is to provide a device capable of efficiently collecting contradictory expressions in units smaller than a sentence.