Abstract: The control of the switches S21 to S28 of the DC/DC converter 120 includes a first mode in which all of the switches S21 to S28 of the DC/DC converter 120 are OFF and a plurality of second modes in which at least one of the switches S21 to S28 of the DC/DC converter 120 is ON, and when switching from the first mode to one of the plurality of second modes, a dead time Td is not provided between the first mode and the one of the plurality of second modes.

Abstract: The present invention may be a method, a computer system, and a computer program product for suggesting an output candidate. The method comprises receiving a user input; selecting a corpus containing an expression similar to the user input among a plurality of corpuses; finding, in the user input, a seed word that may be present in a definition statement of an entry in a dictionary; identifying, in the dictionary, an entry of a definition statement containing the seed word or within a threshold similarity to the seed word with reference to the selected corpus; and suggesting the identified entry as an output candidate.

Abstract: A fast neural transition-based parser. The fast neural transition-based parser includes a decision tree-based classifier and a state vector control loss function. The decision tree-based classifier is dynamically used to replace a multilayer perceptron in the fast neural transition-based parser, and the decision tree-based classifier increases speed of neural transition-based parsing. The state vector control loss function trains the fast neural transition-based parser, the state vector control loss function builds a vector space favorable for building a decision tree that is used for the decision tree-based classifier in the neural transition-based parser, and the state vector control loss function maintains accuracy of neural transition-based parsing while the decision tree-based classifier is used to increase the speed of the neural transition-based parsing while using the decision tree-based classifier to increase the speed of the neural transition-based parsing.

Abstract: A computer-implemented method is provided for suggesting a query term. The method includes obtaining a seed term and a candidate term. The method further includes retrieving two nodes indicating the seed term and the candidate term respectively from a semi-structured corpora. The method also includes determining, by a processor device, whether the candidate term includes details of information on the seed term based on a positional relation between the two nodes in the semi-structured corpora. The method additionally includes suggesting the candidate term as an additional query term in response to a positive determination. The method further includes performing the query with the candidate term as the additional query term in response to a suggestion of use acceptance to enhance query answer retrieval results.

Abstract: A search query is received from a search engine of multiple search engines. One or more search results from each search engine of the multiple search engines in response to the search query are received. Each search result of the one or more search results includes a first search score. A factor is determined for each search result of the one or more search results. A second search score is determined for each search result of the one or more search results based on the first search score and the factor. The one or more search results are displayed to a user of the search engine in an order. The order is based on the second search score.

Abstract: Frequent sequences extracted from a set of documents according to a common rule are obtained. Based on comparing occurrence frequencies of various sequences, confidence of the first frequent sequence being a label expression representing a document part in a target document is evaluated. Keywords are extracted from the target document based on evaluation of the confidence.

Abstract: A technique for presenting a text is disclosed. In the technique, a target text is obtained and a difference summary between the target text and a set of similar texts similar to the target text is prepared. The difference summary includes one or more variable text parts in the target text each varied in at least one text in the set of similar texts and a statistic of varying of each variable text part over the set of similar texts. The one or more variable text parts are marked in the target text based on the statistic of each variable text part. The target text is shown with the one or more variable text parts marked.

Abstract: A method, a system, and a computer program product for predicting an outcome expected for a particular positional value is provided. In the method, an input set of data records, each having a label and a positional value, and a target positional value are obtained. The label of each data record is one in a label set. A learning model that includes an output layer, an input layer corresponding to the label set and a network structure provided therebetween is read. In the learning model, the network structure has a plurality of functions trained so as to evaluate influence from each label in the label set depending on a relationship between the target positional value and a representative positional value associated with the label in the label set. A target outcome is estimated for the target positional value from the input set using the learning model.

Abstract: A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

Abstract: A fast neural transition-based parser. The fast neural transition-based parser includes a decision tree-based classifier and a state vector control loss function. The decision tree-based classifier is dynamically used to replace a multilayer perceptron in the fast neural transition-based parser, and the decision tree-based classifier increases speed of neural transition-based parsing. The state vector control loss function trains the fast neural transition-based parser, the state vector control loss function builds a vector space favorable for building a decision tree that is used for the decision tree-based classifier in the neural transition-based parser, and the state vector control loss function maintains accuracy of neural transition-based parsing while the decision tree-based classifier is used to increase the speed of the neural transition-based parsing while using the decision tree-based classifier to increase the speed of the neural transition-based parsing.

Abstract: A method of creating a presentation includes extracting text parts from content, assigning first attributes to the text parts based on the content and design similarities between the text parts, assigning second attributes to the text parts based on processing the text content to extract contextual similarities between the text parts, determining groups of the text parts based on the first and second attributes, determining first links between the text parts of different groups using the display information and adjacencies of the text parts, determining second links of the text parts to object types defined by a graph restriction listing the object types and their parameters, and outputting a presentation by setting the text parts in the presentation corresponding to the parameters of their linked object type in the graph restriction, wherein the text parts of each of the groups are associated with a same one of the object types.

Abstract: A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

Abstract: A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

Abstract: A computer retrieves a dialog information records of the active call of the first operator. The computer extracts features from the dialog information records. The computer determines a feature vector from the extracted features and determines a transfer probability value based on the feature vector and previous call transfers to the second operator.

Abstract: Embodiments relate to a type of expression based on a particular theme. An aspect includes acquiring, by an electronic apparatus, from text data for learning, a subset of the text data associated with the particular theme and with particular time period information. Another aspect includes extracting text data containing negative information from the acquired subset of the text data. Another aspect includes extracting a word or phrase having a high correlation with the extracted text data or a word or phrase having a high appearance frequency in the extracted text data from the extracted text data. Yet another aspect includes determining that the extracted word or phrase is the type of expression based on the particular theme.

Abstract: The present invention may be a method, a computer system, and a computer program product for suggesting an output candidate. The method comprises receiving a user input; selecting a corpus containing an expression similar to the user input among a plurality of corpuses; finding, in the user input, a seed word that may be present in a definition statement of an entry in a dictionary; identifying, in the dictionary, an entry of a definition statement containing the seed word or within a threshold similarity to the seed word with reference to the selected corpus; and suggesting the identified entry as an output candidate.

Abstract: Methods and systems for searching for similar documents include comparing an input index of a requested document to one or more stored indices for respective stored documents to produce a similarity score for each of the stored documents. Each index indicates which of a plurality of queries matched a respective document. The stored documents are filtered to remove dissimilar documents based on a comparison of each respective similarity score to a threshold. A list of any stored documents that remain after said filtering is output.

Abstract: A first element is extracted from a pair including a past inquiry and a past response, wherein the first element indicates that the past response shows an understanding of the past inquiry. A model is generated used to estimate a second element in a new inquiry based on the first element, wherein the second element indicates that a new response to the new inquiry shows an understanding of the new inquiry.

Abstract: A method and system are provided for collecting social media users who have a specific profile. The method includes retrieving a set of lists connected by at least one criterion to a particular list that is included in a set of reliable lists whose users have already been reliably deemed to have a specific profile. The method includes calculating a list name based confidence value and a list member based confidence value for each list in the retrieved set of lists. The method includes updating the set of reliable lists by adding all lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. The method includes outputting a listing of users belonging to set of reliable lists as the social media users who have the specific profile.

Abstract: A keyword to be categorized is received. A category dictionary including categories having associated registered keywords, and a text corpus are received. Registered keywords are identified in the category dictionary having a degree of similarity to the keyword to be categorized that is equal to or greater than a predetermined value, and the categories associated with the identified registered keywords are extracted. Registered keywords are identified that are co-occurring in the text corpus with the keyword to be categorized, and the categories associated with the identified co-occurring registered keywords are extracted. A degree of importance is determined for each extracted category based on a function of the identified registered keywords in the category dictionary and/or a function of the identified co-occurring registered keywords.