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: 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 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: 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 system including: a text analyzing unit extracting keywords as predetermined facets from text of a document to define a time sequence of a part of the text between the keywords and sort words included in the part into time axes, the time axes being divisions of the time sequence; and a drawing unit drawing a chart in a drawing space, the chart including nodes and an edge, the nodes corresponding to the facets, the nodes being arranged in the drawing space in accordance with a relationship of the time sequence between the facets, the edge being linked to the nodes in accordance with a connection between the facets.

Abstract: A method, computer system, and a computer program product for data analysis using a plurality of map data is provided. The present invention may include generating recommendation data from a plurality of corpus data. The present invention may include receiving a user region corresponding to a geographic area. The present invention may include determining a plurality of nouns from the recommendation data. The present invention may include presenting the plurality of nouns to a user. The present invention may include receiving a plurality of predictor variables. The present invention may include determining a plurality of target variables based on the predictor variables. The present invention may include determining a weight for each predictor variable based on the plurality of target variables. The present invention may include presenting the plurality of predictor variables and the weight of each predictor variable to the user.

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 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 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: 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 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 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 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: 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 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 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 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: 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: 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: 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.