Abstract: Software that extracts contextually relevant terms from a text sample (or corpus) by performing the following steps: (i) identifying a first term from a corpus, based, at least in part, on a set of initial contextual characteristic(s), where each initial contextual characteristic of the set of initial contextual characteristic(s) relates to the contextual use of at least one category related term of a set of category related term(s) in the corpus; (ii) adding the first term to the set of category related term(s), thereby creating a revised set of category related term(s) and a set of first term contextual characteristic(s), where each first term contextual characteristic of the set of first term contextual characteristic(s) relates to the contextual use of the first term in the corpus; and (iii) identifying a second term from the corpus, based, at least in part, on the set of first term contextual characteristic(s).

Abstract: Receiving a search request by a computer. Parsing text-based search terms from the search request. Searching documents in a document index based on the text-based search terms. Transmitting to a user device a list of documents identified by the search. Receiving, from the user device, a selection of one of the identified documents. Parsing executable-based search terms from the search request. Identifying content defining an executable in the selected document. Forming a display element from the executable. Identifying and associating variable names and variable values in the search query with variable names and variable values in the executable. Forming a statement to solve from the search query. Solving the statement to solve. Substituting the variable values and the statement solution for corresponding variable names in the display element. Embedding the display element in the selected document, and transmitting the selected document with the embedded display element to the user device.

Abstract: Receiving a search request by a computer. Parsing text-based search terms from the search request. Searching documents in a document index based on the text-based search terms. Transmitting to a user device a list of documents identified by the search. Receiving, from the user device, a selection of one of the identified documents. Parsing executable-based search terms from the search request. Identifying content defining an executable in the selected document. Forming a display element from the executable. Identifying and associating variable names and variable values in the search query with variable names and variable values in the executable. Forming a statement to solve from the search query. Solving the statement to solve. Substituting the variable values and the statement solution for corresponding variable names in the display element. Embedding the display element in the selected document, and transmitting the selected document with the embedded display element to the user device.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: Methods and arrangements for developing knowledge bases from social media. A question is obtained from social media. Social media are consulted, and a legitimacy of the question is ascertained. All the answers to the question are harvested from the social media including the rich media that is associated with these answers, and the question is filtered out if determined not to be legitimate.

Abstract: Methods and arrangements for developing knowledge bases from social media. A question is obtained from social media. Social media are consulted, and a legitimacy of the question is ascertained. All the answers to the question are harvested from the social media including the rich media that is associated with these answers, and the question is filtered out if determined not to be legitimate.

Abstract: Methods and arrangements for filtering audio in a noisy environment involving receiving audio input at a user's location, using a plurality of audio input devices in proximity with a user. The audio is then separated into sources in response to a user selection. After the selection is made, the amplitudes of the audio sources are adjusted based on the selection. Other variants and embodiments are broadly contemplated herein.

Abstract: A computer-implemented method includes receiving a question. The question is directed to a question answering system. The method includes extracting two or more queries from the question. The method includes generating two or more question sets, each of the two or more question sets addressing all of the two or more queries. The method includes selecting one of the two or more question sets to yield a selected question set, based on analyzing each of the two or more question sets according to a pre-trained model. The method includes presenting the selected question set to the question answering system.

Abstract: Two or more variables in a source code are determined. One or more variable groupings for the two or more variables are determined. A potential error for a variable of the two or more variables is determined based on the determined variable groupings.

Abstract: Generating a natural language dialog by finding missing semantic information in a user question by comparing it to the closest question available in a question corpus. Incrementally improved question precision is targeted during each round of the natural language dialog by generating follow-up questions that clarify semantic and syntactic characteristics of the user question. The follow-up questions are derived from analysis of the user question to identify areas of improvement on the user question.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: A computer-implemented method includes receiving a question. The question is directed to a question answering system. The method includes extracting two or more queries from the question. The method includes generating two or more question sets, each of the two or more question sets addressing all of the two or more queries. The method includes selecting one of the two or more question sets to yield a selected question set, based on analyzing each of the two or more question sets according to a pre-trained model. The method includes presenting the selected question set to the question answering system.

Abstract: Two or more variables in a source code are determined. One or more variable groupings for the two or more variables are determined. A potential error for a variable of the two or more variables is determined based on the determined variable groupings.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: Techniques, systems, and articles of manufacture for utilizing dependency among internet search results. A method includes associating a user search query with a search task, identifying multiple information documents that correspond to the search task, and generating a recommended sequence of the multiple information documents to present to the user in response to the user search query, wherein the recommended sequence is based on dependency information associated with the multiple information documents.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.

Abstract: A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the first confidence score meets a pre-defined threshold.