Abstract: Embodiments relate to generating a retrieval condition for retrieving a target character string from texts by pattern matching. An aspect includes dividing a first text into words. Another aspect includes generating a converted character string by performing at least one of appending at least one character in at least either one of previous and subsequent positions of the target character string. Another aspect includes replacing at least one character of the target character string. Another aspect includes generating the retrieval condition for retrieval candidates in the words of the first text, the retrieval condition comprising determining that a retrieval candidate matches the target character string and does not match the converted character string based on a ratio of a part of the retrieval candidate which matches the converted character string and corresponds to the target character string is less than or equal to a reference frequency.

Abstract: Embodiments relate to a dangerous 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 dangerous expression based on the particular theme.

Abstract: A method includes hierarchically identifying occurrences of some of the words in the set of sentences; creating a first index for each of some of the words based on the upper hierarchy of occurrences identified for each word; receiving input of a queried word; hierarchically identifying occurrences of the queried word in the set of sentences; creating a second index based on the upper hierarchy of occurrences identified for the queried word; comparing the first index and the second index to calculate an estimated value for the number of occurrences of a word in the neighborhood of the queried word; and calculating the actual value of the number of occurrences of a word in the neighborhood of the queried word based on an upper hierarchy and lower hierarchy of the occurrences on condition that the estimated value is equal to or greater than a predetermined number.

Abstract: A method includes hierarchically identifying occurrences of some of the words in the set of sentences; creating a first index for each of some of the words based on the upper hierarchy of occurrences identified for each word; receiving input of a queried word; hierarchically identifying occurrences of the queried word in the set of sentences; creating a second index based on the upper hierarchy of occurrences identified for the queried word; comparing the first index and the second index to calculate an estimated value for the number of occurrences of a word in the neighborhood of the queried word; and calculating the actual value of the number of occurrences of a word in the neighborhood of the queried word based on an upper hierarchy and lower hierarchy of the occurrences on condition that the estimated value is equal to or greater than a predetermined number.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for generating target text based on target data. The method includes one or more processors decomposing one or more portions of text into at least one corresponding keyword and at least one corresponding template. The method further includes learning a classification model associated with selecting a template based on a category of a keyword. The method further includes identifying a target keyword that is represented by target data. The method further includes selecting a target template that is used to represent the target data based on a category associated with the identified target keyword utilizing the classification model. The method further includes generating target text that represents the target data based on the selected text template based on the selected target template and the identified target keyword.

Abstract: An apparatus for calculating a correlation between annotations includes a first obtaining unit configured to provide an annotator with a first data group capable of being evaluated to determine whether or not to attach annotations thereto, and obtaining a plurality of first confidence levels indicating certainty of the annotations in the first data group, the annotator outputting confidence levels indicating certainty of annotations to be attached to data when the data is given; a second obtaining unit configured to provide the annotator with a second data group used to calculate a correlation between the plurality of annotations, and thereby obtaining a plurality of second confidence levels indicating the certainty of the annotations in the second data group; and a computing unit configured to compute an estimated value of the correlation between the plurality of annotations based on the plurality of first and second confidence levels.

Abstract: An apparatus for calculating a correlation between annotations includes a first obtaining unit configured to provide an annotator with a first data group capable of being evaluated to determine whether or not to attach annotations thereto, and obtaining a plurality of first confidence levels indicating certainty of the annotations in the first data group, the annotator outputting confidence levels indicating certainty of annotations to be attached to data when the data is given; a second obtaining unit configured to provide the annotator with a second data group used to calculate a correlation between the plurality of annotations, and thereby obtaining a plurality of second confidence levels indicating the certainty of the annotations in the second data group; and a computing unit configured to compute an estimated value of the correlation between the plurality of annotations based on the plurality of first and second confidence levels.

Abstract: Embodiments relate to generating a retrieval condition for retrieving a target character string from texts by pattern matching. An aspect includes dividing a first text into words. Another aspect includes generating a converted character string by performing at least one of appending at least one character in at least either one of previous and subsequent positions of the target character string. Another aspect includes replacing at least one character of the target character string. Another aspect includes generating the retrieval condition for retrieval candidates in the words of the first text, the retrieval condition comprising determining that a retrieval candidate matches the target character string and does not match the converted character string based on a ratio of a part of the retrieval candidate which matches the converted character string and corresponds to the target character string is less than or equal to a reference frequency.

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.

Abstract: Embodiments relate to a dangerous 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 dangerous expression based on the particular theme.

Abstract: In one embodiment of the present invention, an apparatus may be used for supporting a survey based on information in an Internet medium. The apparatus comprises: a first acquisition hardware unit, wherein the first acquisition hardware unit acquires first evaluation information representing a degree of evaluation acquired by a survey of a real society pertaining to a prescribed target; a second acquisition hardware unit, wherein the second acquisition hardware unit acquires second evaluation information representing a degree of evaluation in the Internet medium pertaining to the prescribed target; and an estimator hardware unit, wherein the estimator hardware unit estimates a bias in information in the Internet medium based on a deviation of the second evaluation information from the first evaluation information.

Abstract: A hardware device analyzes a document that includes a text-based visual representation. A correspondence information hardware storage device holds known representations of graphical images as text-based visual representations. The graphical images depict portraits of physical objects. The text-based visual representations are associated with information that each describe one of the physical objects. An identification hardware device identifies a text-based visual representation within a document. The identification hardware device matches the text-based visual representation within the document to one or more of the text-based visual representations stored in the correspondence information hardware storage device. An editing hardware device retrieves information from the correspondence information hardware storage device that is identified, by the identification hardware device, as describing a text-based visual representation component within the document.

Abstract: A method includes hierarchically identifying occurrences of some of the words in the set of sentences; creating a first index for each of some of the words based on the upper hierarchy of occurrences identified for each word; receiving input of a queried word; hierarchically identifying occurrences of the queried word in the set of sentences; creating a second index based on the upper hierarchy of occurrences identified for the queried word; comparing the first index and the second index to calculate an estimated value for the number of occurrences of a word in the neighborhood of the queried word; and calculating the actual value of the number of occurrences of a word in the neighborhood of the queried word based on an upper hierarchy and lower hierarchy of the occurrences on condition that the estimated value is equal to or greater than a predetermined number.

Abstract: A method includes hierarchically identifying occurrences of some of the words in the set of sentences; creating a first index for each of some of the words based on the upper hierarchy of occurrences identified for each word; receiving input of a queried word; hierarchically identifying occurrences of the queried word in the set of sentences; creating a second index based on the upper hierarchy of occurrences identified for the queried word; comparing the first index and the second index to calculate an estimated value for the number of occurrences of a word in the neighborhood of the queried word; and calculating the actual value of the number of occurrences of a word in the neighborhood of the queried word based on an upper hierarchy and lower hierarchy of the occurrences on condition that the estimated value is equal to or greater than a predetermined number.

Abstract: A computer-implemented method, program product, and system, for extracting pieces of text from a plurality of pieces of text. The method includes: primarily evaluating a measure of positive expressions and a measure of negative expressions included in each of pieces of text; secondarily evaluating each of the pieces of text on the basis of a plurality of evaluation functions, where certain evaluation functions among the plurality of evaluation functions include, as variables, the measure of positive expressions and the measure of negative expressions; and extracting a piece of text having an evaluation result with a higher rating in preference to a piece of text having an evaluation result with a lower rating, where the individual evaluation results are based on the same evaluation function among the plurality of evaluation functions.

Abstract: A computer system of an embodiment of the disclosure can be used to automatically create or populate a terms dictionary using a set of computing units. A morphological analysis unit can acquire token sequence data by performing morphological analysis for the text data. A category distinguishing unit can distinguish tokens of the token sequence data by using a category dictionary to extract uncategorized words. An uncategorized-word comparing unit can compare each of the extracted uncategorized words with an uncategorized-word comparison rule to extract an uncategorized word matching the uncategorized-word comparison rule as a registration candidate word. A token-sequence comparing unit can compare a token sequence of the token sequence data with a token-sequence comparison rule to extract a token sequence matching the token-sequence comparison rule as registration candidate words. A permission unit can permit a user to select whether to register the registration candidate words in the category dictionary.

Abstract: A computer implemented method for creating indices for a database having a plurality of documents each being associated with one or more keywords. The method includes the steps of: dividing the database into a plurality of subsets; separating the keywords into a plurality of keyword groups based upon modulo G of the hash value of the keyword for each subset; reading each document of each subset to create a first sub-index and writing same to a storage device of the computer; reading the first sub-indices to merge the first sub-indices into a second sub-index for each keyword group to write same to the storage device; and reading the second sub-indices from the storage device to merge the second sub-indices into an index for the database and write same on the storage device. A program and a system for creating indices are also provided.

Abstract: A system, method and computer program product for searching at high speed for documents matching a dependency pattern from document data containing a large volume of text documents. The system includes a storage device for storing, index storage means for storing in the storage device occurrence information, receiving means for receiving information, reading means for reading from the index storage means, and searching means for comparing occurrence information. The method and computer program product include the steps of storing in the storage device, receiving information, reading from the storage device, comparing occurrence information, and searching. The computer program product includes instructions to execute the steps of storing each of the plurality of document data in the storage device, storing in the storage device occurrence information.

Abstract: Techniques for analyzing keyword data for quality management purposes are provided. One or more keywords are selected. Each of the one or more keywords represent a category of quality management. A keyword time series is prepared for each of the one or more selected keywords. A set of fixed form time series is prepared for each of the one or more selected keywords. The set of fixed form time series comprises one or more fixed form time series representing statistical data related to the one or more selected keywords. One or more correction sets comprising one or more correction parameters are obtained. Each of the one or more correction parameters correspond to one of the one or more fixed form time series within each set of fixed form time series. A set of corrected time series is generated for each of the one or more correction sets.

Abstract: A system of the present invention stores: a first index which designates lists of keywords contained in texts from identifications of the respective texts; a second index which designates lists of texts containing keywords from identifications of the respective keywords; and the number of texts containing the respective keywords. Then, upon receiving an input of a text search condition, the system calculates an estimation of search time by the first index and an estimation of search time by the second index, and determines which one of the first and second indexes makes a search faster. Then, by using the index which has been determined to make the search faster, the system searches for keywords which appear in texts satisfying the text search condition with higher frequency.