Abstract: According to one embodiment, a data processing apparatus includes a processor provided with hardware. The processor extracts a first event data item, a second event data item, and a third event data item from input first document data. When a first relational data item indicating a presence of transitivity between the first event data item and the second event data item is extracted and a second relational data item indicating a presence of transitivity between the second event data item and the third event data item is extracted, the processor generates a third relational data item indicating a presence of a relation between the first event data item and the third event data item.

Abstract: A symbol sequence converting apparatus according to an embodiment includes one or more hardware processors. The processors: generates a plurality of candidate output symbol sequences, based on rule information in which input symbols are each associated with one or more output symbols each obtained by converting the corresponding input symbol in accordance with a predetermined conversion condition, the plurality of candidate output symbol sequences each containing one or more of the output symbols and corresponding to an input symbol sequence containing one or more of the input symbols; derives respective confidence levels of the plurality of candidate output symbol sequences by using a learning model; and identifies, as an output symbol sequence corresponding to the input symbol sequence, the candidate output symbol sequence corresponding to a highest confidence level.

Abstract: According to an embodiment, an information processing apparatus includes one or more processors. The one or more processors display a relation display screen including text box images each indicating one of a plurality of text ranges included in a document and a relation image indicating a relation between the plurality of text ranges.

Abstract: According to one embodiment, a learning apparatus includes a processor. The processor acquires a document to which a tag is added. The processor converts the document into an image to generate a document image, and converts the tag into an image according to composition of the document image to generate a tag image. The processor trains a network model using the document image as input data and the tag image as ground truth data to generate a trained model.

Abstract: According to one embodiment, an information extraction apparatus includes a processor including hardware. The processor extracts a description of a problematic event from text data. The processor extracts, from the text data, a description of a candidate action for taking an action against the problematic event. The processor discerns an effect of the candidate action against the problematic event.

Abstract: A prophylactic or therapeutic agent for an aneurysm comprising a miR-33b inhibiting substance, preferably an antisense oligonucleotide against miR-33b, as an active ingredient.

Abstract: According to one embodiment, a discrimination apparatus includes a processor. The processor acquires an event indicative of a case that is a processing object, and a document including a plurality of sentences. The processor generates a plurality of subsets in each of which part of the sentences are grouped. The processor discriminates, in regard to each of the subsets, a causal relationship between a sentence included in the subset and the event.

Abstract: A learning device includes one or more processors. The processors input, to an input layer of a neural network including hidden layers defined for respective first arrangement patterns indicating arrangement of one or more words, and output layers connected with some of hidden layers, one or more first morphemes conforming to any of first arrangement patterns, among morphemes included in a document, and learn the neural network to minimize a difference between one or more second morphemes conforming to any of second arrangement patterns indicating arrangement of one or more words, among morphemes included in the document, and output morphemes from the neural network for the input first morphemes. The processors output an embedding vector of the first morphemes that is obtained based on a weight of the learned neural network.

Abstract: According to one embodiment, a data processing apparatus includes a processor provided with hardware. The processor extracts a first event data item, a second event data item, and a third event data item from input first document data. When a first relational data item indicating a presence of transitivity between the first event data item and the second event data item is extracted and a second relational data item indicating a presence of transitivity between the second event data item and the third event data item is extracted, the processor generates a third relational data item indicating a presence of a relation between the first event data item and the third event data item.

Abstract: According to one embodiment, a data generation apparatus includes a processor. The processor selects an event group in which at least a part of a plurality of event ranges overlap, the event ranges being ranges of character sequences estimated by a plurality of different methods with respect to a document of teaching data and being different from ranges of character sequences defined with respect to the document. The processor determines, from among the event group, an additional event which is an event range to be added to the teaching data.

Abstract: A symbol sequence converting apparatus according to an embodiment includes one or more hardware processors. The processors: generates a plurality of candidate output symbol sequences, based on rule information in which input symbols are each associated with one or more output symbols each obtained by converting the corresponding input symbol in accordance with a predetermined conversion condition, the plurality of candidate output symbol sequences each containing one or more of the output symbols and corresponding to an input symbol sequence containing one or more of the input symbols; derives respective confidence levels of the plurality of candidate output symbol sequences by using a learning model; and identifies, as an output symbol sequence corresponding to the input symbol sequence, the candidate output symbol sequence corresponding to a highest confidence level.

Abstract: According to an embodiment, an information processing apparatus includes one or more processors. The one or more processors display a relation display screen including text box images each indicating one of a plurality of text ranges included in a document and a relation image indicating a relation between the plurality of text ranges.

Abstract: An information processing device according to an embodiment includes one or more processors. The processors perform hierarchical clustering of a key phrase group. The processors divide the key phrase group into candidate clusters. The processors receive a selectin operation of one item from predetermined items for classifying the document group. The processors calculate, for each candidate cluster, a score indicating utility with respect to the selected item. The processors decide, as a reference cluster, a candidate cluster for which the score has a predetermined ranking. The processors divide the reference cluster into sub-clusters. The processors extract predetermined sub-items in the lower levels of the selected item. And the processors control presentation of an expansion image for expressing the information volume of the documents for each sub-item and each sub-cluster.

Abstract: A learning device includes one or more processors. The processors input, to an input layer of a neural network including hidden layers defined for respective first arrangement patterns indicating arrangement of one or more words, and output layers connected with some of hidden layers, one or more first morphemes conforming to any of first arrangement patterns, among morphemes included in a document, and learn the neural network to minimize a difference between one or more second morphemes conforming to any of second arrangement patterns indicating arrangement of one or more words, among morphemes included in the document, and output morphemes from the neural network for the input first morphemes. The processors output an embedding vector of the first morphemes that is obtained based on a weight of the learned neural network.

Abstract: An information processing device according to an embodiment includes one or more processors. The processors perform hierarchical clustering of a key phrase group. The processors divide the key phrase group into candidate clusters. The processors receive a selectin operation of one item from predetermined items for classifying the document group. The processors calculate, for each candidate cluster, a score indicating utility with respect to the selected item. The processors decide, as a reference cluster, a candidate cluster for which the score has a predetermined ranking. The processors divide the reference cluster into sub-clusters. The processors extract predetermined sub-items in the lower levels of the selected item. And the processors control presentation of an expansion image for expressing the information volume of the documents for each sub-item and each sub-cluster.

Abstract: According to an embodiment, a work supporting system includes a specification system, a configuration system, and a decision engine. The specification system is implemented by one or more hardware processors and configured to identify work items from a maintenance log to which a work content is written as text. The configuration system is implemented by one or more hardware processors and configured to establish an anteroposterior relation between the work items based on a clue expression indicating time and the anteroposterior relation in the text within a threshold distance of the work items identified by the specification system. The decision engine is implemented by one or more hardware processors and configured to determine an order relation among the work items based on the anteroposterior relation established by the configuration system.

Abstract: According to an embodiment, a text-to-speech device includes a receiver to receive an input text containing a peculiar expression; a normalizer to normalize the input text based on a normalization rule in which the peculiar expression, a normal expression of the peculiar expression, and an expression style of the peculiar expression are associated, to generate normalized texts; a selector to perform language processing of each normalized text, and select a normalized text based on result of the language processing; a generator generate a series of phonetic parameters representing phonetic expression of the selected normalized text; a modifier modifies a phonetic parameter in the normalized text corresponding to the peculiar expression in the input text based on a phonetic parameter modification method according to the normalization rule of the peculiar expression; and a output unit to output a phonetic sound synthesized using the series of phonetic parameters including the modified phonetic parameter.

Abstract: According to one embodiment, an update support apparatus includes a first storage, a second storage, an extractor and a presenter. The first storage stores an identifier, word information, and an update history which are associated with each other. The second storage stores dictionary entries in each of which the identifier and the update history are associated with each other for each version of one or more dictionaries which use the word information. The extractor extracts one or more update candidates from the second storage, the one or more update candidates each being a dictionary entry having the identifier stored in the first storage and an update history different from the update history stored in the first storage. The presenter presents the one or more update candidates to a user.

Abstract: According to one embodiment, an apparatus for supporting reading of a document includes a model storage unit, a document acquisition unit, a feature information extraction, and an utterance style estimation unit. The model storage unit is configured to store a model which has trained a correspondence relationship between first feature information and an utterance style. The first feature information is extracted from a plurality of sentences in a training document. The document acquisition unit is configured to acquire a document to be read. The feature information extraction unit is configured to extract second feature information from each sentence in the document to be read. The utterance style estimation unit is configured to compare the second feature information of a plurality of sentences in the document to be read with the model, and to estimate an utterance style of the each sentence of the document to be read.

Abstract: According to an embodiment, a text-to-speech device includes a receiver to receive an input text containing a peculiar expression; a normalizer to normalize the input text based on a normalization rule in which the peculiar expression, a normal expression of the peculiar expression, and an expression style of the peculiar expression are associated, to generate normalized texts; a selector to perform language processing of each normalized text, and select a normalized text based on result of the language processing; a generator generate a series of phonetic parameters representing phonetic expression of the selected normalized text; a modifier modifies a phonetic parameter in the normalized text corresponding to the peculiar expression in the input text based on a phonetic parameter modification method according to the normalization rule of the peculiar expression; and a output unit to output a phonetic sound synthesized using the series of phonetic parameters including the modified phonetic parameter.