Abstract: A method, device, and computer program product for detecting negative opinions in social media, computer program product, and computer. Negative opinions in social media can be precisely detected at an early stage. A method for processing, with a computer, a plurality of messages sent by a plurality of users over time includes the following steps: obtaining a plurality of messages, each including a specific proper noun; determining a politeness level of each of the plurality of messages, each including the specific proper noun; and calculating a proportion of messages having a politeness level lower than a certain threshold with respect to the plurality of messages, each including the specific proper noun.

Abstract: A computer-implemented method, program, and system for identifying non-self-descriptive terms in electronic documents. The computer-implemented method for identifying a non-self-descriptive term in an electronic document, includes a memory and a processor communicatively coupled to the memory and configured to execute the steps of a method. The method includes acquiring a noun included in the corpus data. The method further includes calculating a qualifying level and a qualified level in the corpus data related to each known in the corpus data. The method further includes identifying one or more nouns included in the corpus data as having a qualifying level and/or qualified level satisfying a predetermined condition. The method further includes presenting a term related to one or more of the nouns in the electronic document as a candidate for the non-self-descriptive term in the electronic document.

Abstract: A computer system, method, and article of manufacture for extracting a term from electronic document data that includes a text segment. The system includes: a first extraction unit that uses a first text processing information to extract a noun word from the document data; a second extraction unit that uses a second text processing information to extract a term candidate in relation to the noun word or a corpus that includes text data described in the same language used in the document data; a weight assignment unit that uses a third text processing information to select which type to assign a weight from the plurality of types and assigns the weight to the selected type for each noun word and term candidate; a determination unit that determines the type to which the noun word and term candidate belong; and an output unit to output the noun word and term candidate.

Abstract: A computer-implemented method is disclosed for determining a calculation expression for finding a KPI relating to a business process (process KPI) on the basis of a KPI obtained in each task (task KPI), the business process being representable as a graph structure including a plurality of tasks and edges representing how processing of the tasks proceeds. The method includes preparing a table defining a group of composite operation expressions for the task KPIs of the plurality of tasks, the composite operation expressions being associated with a predetermined relation of the plurality of tasks for each task KPI type; determining composite operation expressions for the task KPIs over the business process for each task KPI type by referring to the table; and creating a calculation expression for finding the process KPI of the business process using the composite operation expressions for the task KPIs over the business process.

Abstract: A computer-implemented method, program, and system for identifying non-self-descriptive terms in electronic documents. The computer-implemented method for identifying a non-self-descriptive term in an electronic document, includes a memory and a processor communicatively coupled to the memory and configured to execute the steps of a method. The method includes acquiring a noun included in the corpus data. The method further includes calculating a qualifying level and a qualified level in the corpus data related to each known in the corpus data. The method further includes identifying one or more nouns included in the corpus data as having a qualifying level and/or qualified level satisfying a predetermined condition. The method further includes presenting a term related to one or more of the nouns in the electronic document as a candidate for the non-self-descriptive term in the electronic document.

Abstract: A computer system, method, and article of manufacture for extracting a term from electronic document data that includes a text segment. The system includes: a first extraction unit that uses a first text processing information to extract a noun word from the document data; a second extraction unit that uses a second text processing information to extract a term candidate in relation to the noun word or a corpus that includes text data described in the same language used in the document data; a weight assignment unit that uses a third text processing information to select which type to assign a weight from the plurality of types and assigns the weight to the selected type for each noun word and term candidate; a determination unit that determines the type to which the noun word and term candidate belong; and an output unit to output the noun word and term candidate.

Abstract: A method, device, and computer program product for detecting negative opinions in social media, computer program product, and computer. Negative opinions in social media can be precisely detected at an early stage. A method for processing, with a computer, a plurality of messages sent by a plurality of users over time includes the following steps: obtaining a plurality of messages, each including a specific proper noun; determining a politeness level of each of the plurality of messages, each including the specific proper noun; and calculating a proportion of messages having a politeness level lower than a certain threshold with respect to the plurality of messages, each including the specific proper noun.

Abstract: A computer system, method, and article of manufacture for extracting a term from electronic document data that includes a text segment. The system includes: a first extraction unit that uses a first text processing information to extract a noun word from the document data; a second extraction unit that uses a second text processing information to extract a term candidate in relation to the noun word or a corpus that includes text data described in the same language used in the document data; a weight assignment unit that uses a third text processing information to select which type to assign a weight from the plurality of types and assigns the weight to the selected type for each noun word and term candidate; a determination unit that determines the type to which the noun word and term candidate belong; and an output unit to output the noun word and term candidate.

Abstract: A dialog processing system which includes a target expression data extraction unit for extracting a plurality of target expression data each including a pattern matching portion which matches an utterance pattern, which are inputted by an utterance pattern input unit and is an utterance structure derived from contents of field-independent general conversations, among a plurality of utterance data which are inputted by an utterance data input unit and obtained by converting contents of a plurality of conversations in one field; a feature extraction unit for retrieving the pattern matching portions, respectively, from the plurality of target expression data extracted, and then for extracting feature quantity common to the plurality of pattern matching portions; and a mandatory data extraction unit for extracting mandatory data in the one field included in the plurality of utterance data by use of the feature quantities extracted.

Abstract: A computer system, method, and article of manufacture for extracting a term from electronic document data that includes a text segment. The system includes: a first extraction unit that uses a first text processing information to extract a noun word from the document data; a second extraction unit that uses a second text processing information to extract a term candidate in relation to the noun word or a corpus that includes text data described in the same language used in the document data; a weight assignment unit that uses a third text processing information to select which type to assign a weight from the plurality of types and assigns the weight to the selected type for each noun word and term candidate; a determination unit that determines the type to which the noun word and term candidate belong; and an output unit to output the noun word and term candidate.

Abstract: A dialog processing system which includes a target expression data extraction unit for extracting a plurality of target expression data each including a pattern matching portion which matches an utterance pattern, which are inputted by an utterance pattern input unit and is an utterance structure derived from contents of field-independent general conversations, among a plurality of utterance data which are inputted by an utterance data input unit and obtained by converting contents of a plurality of conversations in one field; a feature extraction unit for retrieving the pattern matching portions, respectively, from the plurality of target expression data extracted, and then for extracting feature quantity common to the plurality of pattern matching portions; and a mandatory data extraction unit for extracting mandatory data in the one field included in the plurality of utterance data by use of the feature quantities extracted.