Abstract: Identification of content classes deemed important by users is received. Classifiers are trained on user communication text to recognize content matching content classes. A composite classifier is applied to text of communication among users of a second set of users, including tagging, by the composite classifier, content in text of communication among the users of the second set. The tagging is responsive to the classifiers recognizing their respective content classes in text of communication among the users of the second set.

Abstract: Familiarity-based text classification framework selection is described. A list of participants in an electronic message thread is selected. For each pairing of participants, a familiarity score is determined based on a number of criteria. A familiarity model is formed based on multiple familiarity scores and a text classification framework for the electronic message thread is selected based on the familiarity model.

Abstract: A computer-implemented system, method and computer program product to transform content and to generate a visual indicator of that transformation, by: performing the transformation on the content, wherein the content is transformed from one state to another state; and displaying the visual indicator representative of the transformation adjacent the content; wherein the visual indicator is selected to display the content in a state that existed prior to or at a time when the transformation was performed. As subsequent transformations are performed, additional visual indicators are listed in the order performed. The visual indicator acts as a user interface element that allows a user to view or revert the content to a state that existed prior to or at a time that the transformation was performed. Selection of the visual indicator follows the control to the destination. The visual indicator also serves to relay information sent from the destination.

Abstract: Identification of content classes deemed important by users is received. Classifiers are trained on user communication text to recognize content matching content classes. A composite classifier is applied to text of communication among users of a second set of users, including tagging, by the composite classifier, content in text of communication among the users of the second set. The tagging is responsive to the classifiers recognizing their respective content classes in text of communication among the users of the second set.

Abstract: A method, computer system, and computer program product for calculating a group chat segment duration is provided. The embodiment may include capturing a plurality of group chat messages from a chat message repository. The embodiment may also include determining a probability distribution based on analyzing the captured group chat messages over a time vector. The embodiment may further include calculating a time parameter based on the determined probability distribution. The embodiment may also include calculating a content parameter based on one or more relevant chat topics. The embodiment may further include calculating an attendee parameter based on a plurality of attendees and one or more attendee associations. The embodiment may also include determining a chat duration prediction based on the calculated time parameter, the calculated content parameter, and the calculated attendee parameter.

Abstract: A computer-implemented system, method and computer program product to identify relative availability for a pre-determined team, by: determining an availability intent for each member of the team; determining at least one availability desire for each member of the team; determining an inferred availability state for each member of the team; and conveying the relative availability for each member of the team based on the availability intent, the availability desire, and the inferred availability state by displaying a visual indicator. The visual indicator includes an avatar for a team member decorated by a dot showing the team member's current availability and an arc showing the team member's availability for a period of time extending from the current availability into the future, wherein the dot and the arc have a fill or color representing the team member's availability, and the arc has a length representing the period of time in the future.

Abstract: A computer-implemented system, method and computer program product to transform content and to generate a visual indicator of that transformation, by: performing the transformation on the content, wherein the content is transformed from one state to another state; and displaying the visual indicator representative of the transformation adjacent the content; wherein the visual indicator is selected to display the content in a state that existed prior to or at a time when the transformation was performed. As subsequent transformations are performed, additional visual indicators are listed in the order performed. The visual indicator acts as a user interface element that allows a user to view or revert the content to a state that existed prior to or at a time that the transformation was performed. Selection of the visual indicator follows the control to the destination. The visual indicator also serves to relay information sent from the destination.

Abstract: A method, computer system, and computer program product for calculating a group chat segment duration is provided. The embodiment may include capturing a plurality of group chat messages from a chat message repository. The embodiment may also include determining a probability distribution based on analyzing the captured group chat messages over a time vector. The embodiment may further include calculating a time parameter based on the determined probability distribution. The embodiment may also include calculating a content parameter based on one or more relevant chat topics. The embodiment may further include calculating an attendee parameter based on a plurality of attendees and one or more attendee associations. The embodiment may also include determining a chat duration prediction based on the calculated time parameter, the calculated content parameter, and the calculated attendee parameter.

Abstract: A method, computer system, and computer program product for calculating a group chat segment duration is provided. The embodiment may include capturing a plurality of group chat messages from a chat message repository. The embodiment may also include determining a probability distribution based on analyzing the captured group chat messages over a time vector. The embodiment may further include calculating a time parameter based on the determined probability distribution. The embodiment may also include calculating a content parameter based on one or more relevant chat topics. The embodiment may further include calculating an attendee parameter based on a plurality of attendees and one or more attendee associations. The embodiment may also include determining a chat duration prediction based on the calculated time parameter, the calculated content parameter, and the calculated attendee parameter.

Abstract: A method, computer system, and computer program product for cognitively summarizing one or more collaborative moments is provided. An embodiment may include receiving a plurality of communications sent during a collaborative session. The embodiment may also include grouping the plurality of received communications into one or more bursts. The embodiment may further include assigning one or more keywords or one or more entities to each burst. The embodiment may also include generating a summary for each burst using one or more natural language processing techniques based on the assigned one or more keywords or one or more entities. The embodiment may further include transmitting the generated summary to each user participating in the collaborative session.

Abstract: Identification of content classes deemed important by users is received. Classifiers are trained on user communication text to recognize content matching content classes. A composite classifier is applied to text of communication among users of a second set of users, including tagging, by the composite classifier, content in text of communication among the users of the second set. The tagging is responsive to the classifiers recognizing their respective content classes in text of communication among the users of the second set.

Abstract: Familiarity-based text classification framework selection is described. A list of participants in an electronic message thread is selected. For each pairing of participants, a familiarity score is determined based on a number of criteria. A familiarity model is formed based on multiple familiarity scores and a text classification framework for the electronic message thread is selected based on the familiarity model.

Abstract: Identification of content classes deemed important by users is received. Classifiers are trained on user communication text to recognize content matching content classes. A composite classifier is applied to text of communication among users of a second set of users, including tagging, by the composite classifier, content in text of communication among the users of the second set. The tagging is responsive to the classifiers recognizing their respective content classes in text of communication among the users of the second set.

Abstract: A method, computer system, and computer program product for cognitively summarizing one or more collaborative moments is provided. An embodiment may include receiving a plurality of communications sent during a collaborative session. The embodiment may also include grouping the plurality of received communications into one or more bursts. The embodiment may further include assigning one or more keywords or one or more entities to each burst. The embodiment may also include generating a summary for each burst using one or more natural language processing techniques based on the assigned one or more keywords or one or more entities. The embodiment may further include transmitting the generated summary to each user participating in the collaborative session.

Abstract: Message grouping using temporal and multi-factor similarity includes grouping multiple messages of a corpus in a group messaging system into a number of message bursts. Each message burst includes a number of messages that have a temporal relationship. Multiple of the number of message bursts are grouped into a message cluster. The grouping is based on a similarity of the number of message bursts as defined by multiple features of the message bursts.

Abstract: A method, computer system, and computer program product for calculating a group chat segment duration is provided. The embodiment may include capturing a plurality of group chat messages from a chat message repository. The embodiment may also include determining a probability distribution based on analyzing the captured group chat messages over a time vector. The embodiment may further include calculating a time parameter based on the determined probability distribution. The embodiment may also include calculating a content parameter based on one or more relevant chat topics. The embodiment may further include calculating an attendee parameter based on a plurality of attendees and one or more attendee associations. The embodiment may also include determining a chat duration prediction based on the calculated time parameter, the calculated content parameter, and the calculated attendee parameter.