Abstract: A method for expanding an answer key to verify a question and answer system is provided in the illustrative embodiments. A definition is constructed of an extended answer type. The extended answer type represents an answer type of an unrepresented answer. The unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key. The extended answer type is created in the answer key according to the definition. The extended answer type is populated such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key. The populated extended answer type in the extended answer key is used to verify that a generated answer from the Q and A system is correct.

Abstract: A system, and computer program product for expanding an answer key to verify a question and answer system are provided in the illustrative embodiments. A definition is constructed of an extended answer type. The extended answer type represents an answer type of an unrepresented answer. The unrepresented answer is unrepresented in the answer key as a valid response to a question in a set of valid responses to the question in the answer key. The extended answer type is created in the answer key according to the definition. The extended answer type is populated such that the unrepresented answer becomes as additional valid response to the question, the creating and the populating extending the answer key to form an extended answer key. The populated extended answer type in the extended answer key is used to verify that a generated answer from the Q and A system is correct.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: A term of use policy document defines permissible actions that may be implemented by a user using a computing device. A natural language processing (NLP)-based question and answer (Q&A) system is trained to understand the policy document. The device includes a management application that interacts with the Q&A system to identify a policy violation. When the user performs an action on the device, the application converts that action into an NLP query directed to the Q&A system to determine whether the action constitutes a violation. The query may be accompanied by metadata associated with the user, the device or its state. Upon receipt of the query and any associated metadata, the Q&A system determines if the user action is compliant with the policy and returns a response. Based on the response, the user's computing device may take an enforcement action, e.g., restricting or disabling functionality, or issuing a warning.

Abstract: A computing device includes a query formulation engine having a data collection component that collects metadata associated with the device or its operation. Typically, the metadata describes a characteristic about the device (e.g., which components or applications are resident, their current operating states or characteristics, what applications are active, which application has the display focus, what permissions are associated with each application, what application-specific calls are being made to the device operating system, etc.). A natural language processing (NLP)-based question and answer (Q&A) system is trained to understand natural language text queries generated by the query formulation engine. When a user performs an action on the device, the engine converts that action, preferably together with a structured form of the metadata, into an NLP query. The query is directed to the Q&A system. A response to the NLP query is received at the computing device and then acted upon.

Abstract: A method, system, and computer program product for bypassing as portion of content of a media. A history of skipped sections for a media is accumulated. Each portion of skipped content is detected during a display of the media. A reason is automatically determined for each portion of media content that is skipped based on a set of predefined criteria and resulting in a measured value for each portion of skipped content. Each portion of skipped content is tagged with the associated measured value. Information associated with the tagged portions of skipped content is shared across a social network for collaborative reuse. A determination is also made whether or not the previously skipped portion or media content exceeds a predetermined user display threshold. Responsive to a user-configurable policy for bypassing content, the content of the media is automatically displayed on a user device and content exceeding the predetermined display threshold is skipped.

Abstract: Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

Abstract: An event associated with a viewable area of an encapsulated component that represents at least a portion of content associated with a user interface component is detected. A determination is made that the event will result in at least a portion of the viewable area of the encapsulated component being outside of a viewable area of the user interface component. The content associated with the user interface component is adjusted automatically to display at least the portion of the viewable area of the encapsulated component within the viewable area of the user interface component.

Abstract: An event associated with a viewable area of an encapsulated component that represents at least a portion of content associated with a user interface component is detected. A determination is made that the event will result in at least a portion of the viewable area of the encapsulated component being outside of a viewable area of the user interface component. The content associated with the user interface component is adjusted automatically to display at least the portion of the viewable area of the encapsulated component within the viewable area of the user interface component.