Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: One or more table content documents (TCDs) can be constructed for a set of tabular data or portion thereof. A set of query features corresponding to a question can be matched to one or more TCDs. A respective candidate answer can be generated for each of the one or more TCDs having a set of features matching the set of query features above a threshold. Zero or more candidate answers can be output to a user consumable data object.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: Model element characteristic preservation is provided when instantiating unified modeling language elements of a unified modeling language model. Requirements management tool elements become associated with a stereotype via, for instance, user assignment of the stereotype to a requirements type of the requirements management project. A stereotype assignment is obtained for a requirements management tool element, and the stereotype is automatically applied when instantiating a UML element representative of the requirements management tool element. Nesting and traceability relationships between the requirements management tool element and other requirements management tool elements are additionally preserved.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.

Abstract: One or more table content documents (TCDs) can be constructed for a set of tabular data or portion thereof. A set of query features corresponding to a question can be matched to one or more TCDs. A respective candidate answer can be generated for each of the one or more TCDs having a set of features matching the set of query features above a threshold. Zero or more candidate answers can be output to a user consumable data object.

Abstract: One or more table content documents (TCDs) can be constructed for a set of tabular data or portion thereof. A set of query features corresponding to a question can be matched to one or more TCDs. A respective candidate answer can be generated for each of the one or more TCDs having a set of features matching the set of query features above a threshold. Zero or more candidate answers can be output to a user consumable data object.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may also include performing structural analysis on the extracted table. The method may further include selecting a structural template based on the structural analysis of the extracted table. Additionally, the method may include selecting a narration template based on the selected structural template. The method may further include narrating the extracted table based on the selected narration template and displaying a narrative output of the extracted table.

Abstract: A method for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The method may include parsing the extracted table. The method may also include performing structural analysis on the parsed extracted table. The method may further include selecting at least one structural template based on the structural analysis of the parsed extracted table. Additionally, the method may include selecting the at least one narration template based on the at least one selected structural template. The method may also include applying the at least one selected narration template to the extracted table. The method may further include narrating the extracted table based on the applying of the at least one selected narration template to the extracted table.

Abstract: A computer system for narrating a table using at least one narration template, wherein the table is extracted from a data source is provided. The computer system may include parsing the extracted table. The computer system may also include performing structural analysis on the parsed extracted table. The computer system may further include selecting at least one structural template based on the structural analysis of the parsed extracted table. Additionally, the computer system may include selecting the at least one narration template based on the at least one selected structural template. The computer system may also include applying the at least one selected narration template to the extracted table. The computer system may further include narrating the extracted table based on the applying of the at least one selected narration template to the extracted table.

Abstract: A method and system for improving location specific (L) functions is provided. The method includes generating and maintaining an online L-word dictionary. Location specific attributes, settings, and preferences associated with a current location of a mobile device and a user are retrieved. An input question is received and an NLP analysis with respect to the input question is executed to extract a required value phrase. An interim question based on an extracted required value phrase is formed and an answer to the input question is determined in natural language based on a solved interim question. The answer is narrated in natural language and includes an automated interactive response from a hardware device in real time.

Abstract: One or more table content documents (TCDs) can be constructed for a set of tabular data or portion thereof. A set of query features corresponding to a question can be matched to one or more TCDs. A respective candidate answer can be generated for each of the one or more TCDs having a set of features matching the set of query features above a threshold. Zero or more candidate answers can be output to a user consumable data object.

Abstract: A method and system for improving location specific (L) functions is provided. The method includes generating and maintaining an online L-word dictionary. Location specific attributes, settings, and preferences associated with a current location of a mobile device and a user are retrieved. An input question is received and an NLP analysis with respect to the input question is executed to extract a required value phrase. An interim question based on an extracted required value phrase is formed and an answer to the input question is determined in natural language based on a solved interim question. The answer is narrated in natural language and includes an automated interactive response from a hardware device in real time.

Abstract: One or more table content documents (TCDs) can be constructed for a set of tabular data or portion thereof. A set of query features corresponding to a question can be matched to one or more TCDs. A respective candidate answer can be generated for each of the one or more TCDs having a set of features matching the set of query features above a threshold. Zero or more candidate answers can be output to a user consumable data object.

Abstract: An approach is provided for managing a deployment of a software package. First and second quality assurance (QA) seals are determined to match exactly one QA seal and no QA seal, respectively, in a plurality of QA seals. First details about hardware, middleware, and applications required in a first environment, which are included in the first QA seal, are determined to match second details of the first environment. Based on the first QA seal matching exactly one QA seal and the first details matching the second details, the software package is determined to be compatible with the first environment and a notification is generated authorizing the deployment of the software package to the first environment. Based on the second QA seal not matching any of the plurality of QA seals, a notification is generated indicating that the deployment of the software package to a second environment is not authorized.

Abstract: A method providing an answer to an input question containing at least one time-sensitive word or at least one time-sensitive phrase using natural language processing (NLP) is provided. The method may include receiving the input question. The method may also include performing natural language processing (NLP) analysis on the input question to extract a required value phrase. The method may further include forming at least one mathematical equation based on the extracted required value phrase. Additionally, the method may include forming at least one interim question based on the extracted required value phrase. The method may further include solving the at least one formed mathematical equation and the at least one formed interim question. The method may also include narrating the answer to the input question in natural language based on the solved at least one interim question or the solved at least one mathematical equation.

Abstract: Generating distractors for text-based MCT items. An MCT item stem is received. The stem is transmitted to a QA system and a plurality of candidate answers related to the stem is received from the QA system. Incorrect answers in the plurality of candidate answers are identified. Textual features are extracted from the stem. A set of semantic criteria associated with the extracted textual features is generated. Based on the generated semantic criteria, a subset of the incorrect candidate answers is selected.