Abstract: Media, methods, and systems are disclosed for contextual discovery and design of application workflow. An event message associated with a user interface control and with an instance of an event is displayed in a communication channel of a group-based communications system. In response to detecting an actuation, by a user, of the user interface control associated with the event message, a workflow creation interface is displayed to configure a new workflow triggered by an instance of an event associated with the event message. Inputs are received from the user, such that the inputs are associated with one or more user-created workflow operations. In response to detecting a triggering event corresponding to the new workflow, the new workflow is executed to carry out the user-created workflow operations.

Abstract: Media, methods, and systems are disclosed for contextual discovery and design of application workflow. An event message associated with a user interface control and with an instance of an event is displayed in a communication channel of a group-based communications system. In response to detecting an actuation, by a user, of the user interface control associated with the event message, a workflow creation interface is displayed to configure a new workflow triggered by an instance of an event associated with the event message. Inputs are received from the user, such that the inputs are associated with one or more user-created workflow operations. In response to detecting a triggering event corresponding to the new workflow, the new workflow is executed to carry out the user-created workflow operations.

Abstract: Media, methods, and systems are disclosed for contextual discovery and design of application workflow. An event message associated with a user interface control and with an instance of an event is displayed in a communication channel of a group-based communications system. In response to detecting an actuation, by a user, of the user interface control associated with the event message, a workflow creation interface is displayed to configure a new workflow triggered by an instance of an event associated with the event message. Inputs are received from the user, such that the inputs are associated with one or more user-created workflow operations. In response to detecting a triggering event corresponding to the new workflow, the new workflow is executed to carry out the user-created workflow operations.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Techniques involving visual display of information related to matching user utterances against graph patterns are described. In one or more implementations, an utterance of a user is obtained that has been indicated as corresponding to a graph pattern through linguistic analysis. The utterance is displayed in a user interface as a representation of the graph pattern.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Systems, device and techniques are disclosed for publishing multiple versions of an application to an application market, via an application programming interface. The application programming interface may be configured to allow automated uploads of multiple version of an application without requiring individual uploads of each version. A developer associated party may provide the multiple versions of the application via the application programming interface. The multiple versions of the applications may be provided via the application programming interface and not an application market interface. The multiple versions may be published in an application to different set of users.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Implementations of the disclosed subject matter provide systems and methods for providing one or more versions of an application to one or more subsets of users based on measured performance of the one or more versions of the application. A method may include providing a first version of an application to a first subset of users of the application. Next, performance of the first version of the application may be measured and a second subset of users of the application may be selected based on the performance of the first version of the application. As a result, a second version of the application may be provided to the second subset of users of the application.

Abstract: Implementations of the disclosed subject matter provide systems and methods for providing one or more versions of an application to one or more subsets of users based on measured performance of the one or more versions of the application. A method may include providing a first version of an application to a first subset of users of the application. Next, performance of the first version of the application may be measured and a second subset of users of the application may be selected based on the performance of the first version of the application. As a result, a second version of the application may be provided to the second subset of users of the application.

Abstract: Implementations of the disclosed subject matter provide systems and methods for providing one or more versions of an application to one or more subsets of users based on measured performance of the one or more versions of the application. A method may include providing a first version of an application to a first subset of users of the application. Next, performance of the first version of the application may be measured and a second subset of users of the application may be selected based on the performance of the first version of the application. As a result, a second version of the application may be provided to the second subset of users of the application.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Techniques involving visual display of information related to matching user utterances against graph patterns are described. In one or more implementations, an utterance of a user is obtained that has been indicated as corresponding to a graph pattern through linguistic analysis. The utterance is displayed in a user interface as a representation of the graph pattern.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.

Abstract: Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.