Abstract: An information processing apparatus includes a lexical analysis unit that generates a training word string, a group generation unit that generates a plurality of training word groups, a matrix generation unit that generates, for each training word group, a training matrix in which a plurality of words and respective semantic vectors of the words are associated, a classification unit that calculates, for a word of each position of the training word string, a probability of the word corresponding to a specific word, using the training matrices generated by the matrix generation unit and a determination model that uses a convolutional neural network, and an optimization processing unit that updates parameters of the determination model, such that the probability of the word labeled as corresponding to the specific word is high, among the probabilities of the words of the respective positions of the training word string calculated by the classification unit.

Abstract: An information processing apparatus includes a lexical analysis unit that generates a training word string, a pair generation unit that generates a plurality of training word pairs, a matrix generation unit that generates, for each training word pair, a training matrix in which a plurality of words and respective semantic vectors of the words are associated, a classification unit that calculates, for a word of each position of the training word string, a probability of the word corresponding to a specific word, using the training matrices generated by the matrix generation unit and a determination model that uses a convolutional neural network, and an optimization processing unit that updates parameters of the determination model, such that the probability of the word labeled as corresponding to the specific word is high, among the probabilities of the words of the respective positions of the training word string calculated by the classification unit.

Abstract: An information processing apparatus includes a lexical analysis unit that generates a training word string, a pair generation unit that generates a plurality of training word pairs, a matrix generation unit that generates, for each training word pair, a training matrix in which a plurality of words and respective semantic vectors of the words are associated, a classification unit that calculates, for a word of each position of the training word string, a probability of the word corresponding to a specific word, using the training matrices generated by the matrix generation unit and a determination model that uses a convolutional neural network, and an optimization processing unit that updates parameters of the determination model, such that the probability of the word labeled as corresponding to the specific word is high, among the probabilities of the words of the respective positions of the training word string calculated by the classification unit.

Abstract: An information processing apparatus includes a lexical analysis unit that generates a training word string, a group generation unit that generates a plurality of training word groups, a matrix generation unit that generates, for each training word group, a training matrix in which a plurality of words and respective semantic vectors of the words are associated, a classification unit that calculates, for a word of each position of the training word string, a probability of the word corresponding to a specific word, using the training matrices generated by the matrix generation unit and a determination model that uses a convolutional neural network, and an optimization processing unit that updates parameters of the determination model, such that the probability of the word labeled as corresponding to the specific word is high, among the probabilities of the words of the respective positions of the training word string calculated by the classification unit.

Abstract: The present invention is a text mining system comprising a synonym cluster acquiring section configured to acquire synonym clusters from texts in text data to be analyzed, the synonym clusters each being a collection of synonymous texts, an implication relationship acquiring section configured to acquire implication relationships among the synonym clusters, and an implication graph generating section configured to generate an implication graph including vertices of synonym clusters and directed edges each indicating a direction from an implied synonym cluster to an implying synonym cluster from the implication relationships among the synonym clusters.

Abstract: A text processing system that is able to appropriately determine textual entailment between sentences with high coverage is provided. The text processing system is configured to execute: processing of extracting a common substructure that is a partial structure of a same type, the partial structure being common to a first sentence and a second sentence and, based on the a structure representing the first sentence and a structure representing the second sentence; processing of extracting at least one of a feature amount representing a dependency relationship between the at least one common substructure in the first and second sentences and a feature amount representing a dependency relationship between the common substructure in the first and second sentences and a substructure different from the common substructure; and processing of determining an entailment relationship between the first sentence and the second sentence by using the extracted feature amount.

Abstract: A method for classifying a new instance including a text document by using training instances with class including labeled data and zero or more training instances with class including unlabeled data, comprising: estimating a word distribution for each class by using the labeled data and the unlabeled data; estimating a background distribution and a degree of interpolation between the background distribution and the word distribution by using the labeled data and the unlabeled data; calculating two probabilities for that the word generated from the word distribution and the word generated from the background distribution; combining the two probabilities by using the interpolation; combining the resulting probabilities of all words to estimate a document probability for the class that indicates the document is generated from the class; and classifying the new instance as a class for which the document probability is the highest.

Abstract: A text mining device (2) is used in which data composed of a set of records including an attribute value and text data is used as analysis target data. The text mining device (2) includes an analysis perspective candidate generation unit (20) that extracts an attribute value from the analysis target data and generates an analysis perspective candidate using the extracted attribute value, and a characteristic degree calculation unit (21) that compares text data in a record including the attribute value extracted as the analysis perspective candidate with text data in a record set that includes at least a record other than the record including the attribute value in the analysis target data, and calculates a characteristic degree indicating a relationship between the analysis perspective candidate and the analysis target data based on a result of the comparison.

Abstract: A reasoning system that enables reasoning when there is a shortage of knowledge. An input unit receives a start state and an end state. A rule candidate generation unit identifies a first state, obtained by tracking one or more known rules from the start state, and a second state, obtained by backtracking one or more known rules from the end state, respectively. The generation unit generates a rule candidate relating to the first state and the second state or generates a rule candidate relating to the first state and a rule candidate relating to the second state. A rule selection unit selects, based on feasibility of the generated rule candidate, which is calculated based on one or more known rules, the generated rule candidate as a new rule. A derivation unit derives the end state from the start state, based on one or more known rules and the new rule.

Abstract: Provided is a text processing system capable of classifying a plurality of texts into groups whose overviews are able to be grasped and classifying texts semantically having entailment relation into the same group even if the texts are not determined to have the entailment relation. Entailment recognition means 71 performs entailment recognition between texts on given texts. Group generation means 72 selects an individual text and generates a group including texts entailing the selected text as members. Group integration means 73 integrates groups in the case where groups satisfy a predetermined condition based on the degree of overlap of members between groups.

Abstract: A text processing system that is able to appropriately determine textual entailment between sentences with high coverage is provided. The text processing system is configured to execute: processing of extracting a common substructure that is a partial structure of a same type, the partial structure being common to a first sentence and a second sentence and, based on the a structure representing the first sentence and a structure representing the second sentence; processing of extracting at least one of a feature amount representing a dependency relationship between the at least one common substructure in the first and second sentences and a feature amount representing a dependency relationship between the common substructure in the first and second sentences and a substructure different from the common substructure; and processing of determining an entailment relationship between the first sentence and the second sentence by using the extracted feature amount.

Abstract: A document classification method includes a first step for calculating smoothing weights for each word and a fixed class, a second step for calculating smoothed second-order word probability, and a third step for classifying document including calculating the probability that the document belongs to the fixed class.

Abstract: Provided is a text processing system which, when an attribute corresponding to one tabulation axis is set, is capable of generating a text group which will produce non-obvious tabulation results when cross-tabulation is performed using that attribute. At the time of input of respective attribute values of an attribute which corresponds to a tabulation axis in cross tabulation and a document associated with any one of the attribute values of the attribute, text extraction means 71 extracts portion not including the attribute value of the attribute from each text obtained by dividing the document into predetermined units. Group generation means 72 performs entailment recognition between texts on the extracted texts and groups texts having an entailment relation.

Abstract: Provided is a text processing system capable of classifying a plurality of texts into groups whose overviews are able to be grasped and classifying texts semantically having entailment relation into the same group even if the texts are not determined to have the entailment relation. Entailment recognition means 71 performs entailment recognition between texts on given texts. Group generation means 72 selects an individual text and generates a group including texts entailing the selected text as members. Group integration means 73 integrates groups in the case where groups satisfy a predetermined condition based on the degree of overlap of members between groups.

Abstract: A method for classifying a new instance including a text document by using training instances with class including labeled data and zero or more training instances with class including unlabeled data, comprising: estimating a word distribution for each class by using the labeled data and the unlabeled data; estimating a background distribution and a degree of interpolation between the background distribution and the word distribution by using the labeled data and the unlabeled data; calculating two probabilities for that the word generated from the word distribution and the word generated from the background distribution; combining the two probabilities by using the interpolation; combining the resulting probabilities of all words to estimate a document probability for the class that indicates the document is generated from the class; and classifying the new instance as a class for which the document probability is the highest.

Abstract: A conversation-sentence generation device according to the invention of this application includes: an input unit that receives, as input information, a conversation sentence given from a user to an agent, and clue information based on which a physical and psychological state of the agent is estimated; an agent state storing unit that stores the physical and psychological state of the agent as an agent state; an agent state estimating unit that estimates a new agent state based on the input information and the agent state; an utterance intention generating unit that generates, based on the input information and the agent state, an utterance intention directed from the agent to the user; a conversation sentence generating unit that generates, based on the input information, the agent state, and the utterance intention, a conversation sentence given from the agent to the user; and an output unit that outputs the conversation sentence generated by the conversation sentence generating unit.

Abstract: An entailment evaluation device includes: a generation unit which generates first information indicating at least the order of occurrence of events of first and second simple sentences included in the hypothesis text and generates second information indicating at least the order of occurrence of events of third and fourth simple sentences included in a target text, the third simple sentence being related to the first simple sentence, the fourth simple sentence being related to the second simple sentence; a calculation unit which obtains a calculation result by comparing, based on the first and second information, the order of occurrence of events of first and second simple sentences and order of occurrence of events of third and fourth simple sentences; and a determination unit which determines, based on at least the calculation result, whether or not the target text entails the hypothesis text.

Abstract: A synonym relation determination device comprises: a synonym expression candidate storage unit which associates and stores a synonym candidate (EW) with the synonym source (OW); a text gathering unit which associates and gathers text with an issuing time; a synonym candidate search unit which calculates from the issuing time of the text a time interval (PD) in which the synonym candidate is searched in a text set (TX); a synonym source search unit which searches for a synonym source from the text set of a period which overlaps with the time interval in which the synonym candidate is searched for and calculates an occurrence of the synonym source; and synonym relation extraction unit which, when the occurrence of the synonym source is present in the time interval in which the synonym candidate is searched for, extracts a synonym relation between the synonym candidate and the synonym source.

Abstract: To provide an implication determining device, an implication determining method, and an implication determining program capable of improving implication determination performance. A new fact determination unit determines whether a given hypothesis is a new fact that indicates a first revealed fact in a hypothesis implied sentence that is a sentence implying the given hypothesis based on a specific expression written in the hypothesis implied sentence. An implication determination unit determines whether the given hypothesis is implied in a sentence to be determined that is a sentence to be determined whether the hypothesis is included.

Abstract: A classification dictionary generation apparatus includes: a lower threshold storage unit that stores lower threshold information that determines a lower threshold of dimensional values of a classification dictionary for classifying a category of a document; and a control unit that generates the classification dictionary based on learning data whose category is known, wherein the control unit generates, based on the lower threshold information stored in the lower threshold storage unit, the classification dictionary in which all of the dimensional values are equal to or larger than the lower threshold.