Abstract: A method for selecting an embedded experience gadget may include displaying an activity stream of a user on a social network, the activity stream comprising a plurality of events and detecting selection of an event from the plurality of events by the user. The method may also include determining a level of intersection between the selected event and each of a plurality of known gadgets and finding an optimal embedded experience gadget from the plurality of known gadgets based on the level of intersection between the selected event and each known gadget. The method may additionally include associating the optimal embedded experience gadget with the selected event and displaying the optimal embedded experience gadget in association with the selected event.

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: 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: 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: 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: A Bluetooth system is comprised of a Bluetooth device and multiple network devices connectable to the Bluetooth device. This Bluetooth device can selectively switch connection pairings with devices in the network based on some triggering event. The switching function automatically occurs between the Bluetooth and network devices without any user intervention. This pairing enables seamless collaboration between devices owned by an owner based on the power settings and usage of the system without needing the user's interaction.

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 method for selecting an embedded experience gadget may include displaying an activity stream of a user on a social network, the activity stream comprising a plurality of events and detecting selection of an event from the plurality of events by the user. The method may also include determining a level of intersection between the selected event and each of a plurality of known gadgets and finding an optimal embedded experience gadget from the plurality of known gadgets based on the level of intersection between the selected event and each known gadget. The method may additionally include associating the optimal embedded experience gadget with the selected event and displaying the optimal embedded experience gadget in association with the selected event.

Abstract: A method for selecting an embedded experience gadget may include displaying an activity stream of a user on a social network, the activity stream comprising a plurality of events and detecting selection of an event from the plurality of events by the user. The method may also include determining a level of intersection between the selected event and each of a plurality of known gadgets and finding an optimal embedded experience gadget from the plurality of known gadgets based on the level of intersection between the selected event and each known gadget. The method may additionally include associating the optimal embedded experience gadget with the selected event and displaying the optimal embedded experience gadget in association with the selected event.

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: 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 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: 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 Bluetooth system is comprised of a Bluetooth device and multiple network devices connectable to the Bluetooth device. This Bluetooth device can selectively switch connection pairings with devices in the network based on some triggering event. The switching function automatically occurs between the Bluetooth and network devices without any user intervention. This pairing enables seamless collaboration between devices owned by an owner based on the power settings and usage of the system without needing the user's interaction.

Abstract: A method for selecting an embedded experience gadget may include displaying an activity stream of a user on a social network, the activity stream comprising a plurality of events and detecting selection of an event from the plurality of events by the user. The method may also include determining a level of intersection between the selected event and each of a plurality of known gadgets and finding an optimal embedded experience gadget from the plurality of known gadgets based on the level of intersection between the selected event and each known gadget. The method may additionally include associating the optimal embedded experience gadget with the selected event and displaying the optimal embedded experience gadget in association with the selected event.

Abstract: A method for selecting an embedded experience gadget may include displaying an activity stream of a user on a social network, the activity stream comprising a plurality of events and detecting selection of an event from the plurality of events by the user. The method may also include determining a level of intersection between the selected event and each of a plurality of known gadgets and finding an optimal embedded experience gadget from the plurality of known gadgets based on the level of intersection between the selected event and each known gadget. The method may additionally include associating the optimal embedded experience gadget with the selected event and displaying the optimal embedded experience gadget in association with the selected event.