Abstract: Systems and methods develop a natural language interface. Conversational data including user utterances is received for a plurality of conversations from a natural language interface. Each of the conversations is classified to determine intents for each user utterance, and for each of the conversations, a control flow diagram showing the intents and sequential flow of the conversation is generated. Each of the control flow diagrams is processed to generate a graph embedding representative of the conversation. A previous conversation that is similar to the current conversation is identified from a previous graph embedding that is nearest to a current graph embedding of a most recent utterance in a current conversation. A previous outcome of the previous conversation is used to predict an outcome of the current conversation, which, when not positive, may control response outputs of the natural language interface to steer the current conversation towards a positive result.

Abstract: Systems and methods develop a natural language interface. Conversational data including user utterances is received for a plurality of conversations from a natural language interface. Each of the conversations is classified to determine intents for each user utterance, and for each of the conversations, a control flow diagram showing the intents and sequential flow of the conversation is generated. Each of the control flow diagrams is processed to generate a graph embedding representative of the conversation. A previous conversation that is similar to the current conversation is identified from a previous graph embedding that is nearest to a current graph embedding of a most recent utterance in a current conversation. A previous outcome of the previous conversation is used to predict an outcome of the current conversation, which, when not positive, may control response outputs of the natural language interface to steer the current conversation towards a positive result.

Abstract: Systems and methods develop a natural language interface. Conversational data including user utterances is received for a plurality of conversations from a natural language interface. Each of the conversations is classified to determine intents for each user utterance, and for each of the conversations, a control flow diagram showing the intents and sequential flow of the conversation is generated. Each of the control flow diagrams is processed to generate a graph embedding representative of the conversation. A previous conversation that is similar to the current conversation is identified from a previous graph embedding that is nearest to a current graph embedding of a most recent utterance in a current conversation. A previous outcome of the previous conversation is used to predict an outcome of the current conversation, which, when not positive, may control response outputs of the natural language interface to steer the current conversation towards a positive result.

Abstract: Methods and systems for measuring and predicting content dissemination in social networks includes computing a “virality score” for popularity of social media content, a “pattern” of diffusion of the content, and a “hype” parameter of such content without requiring a “friendship graph” or “information diffusion” structured data. It also includes an iterative, predictive model, which predicts future performance (or future rate of dissemination) of the content while mitigating a class imbalance problem inherent in predicting viral posts, and which provides updates to the model based on actual performance results.

Abstract: Systems and methods develop a natural language interface. Conversational data including user utterances is received for a plurality of conversations from a natural language interface. Each of the conversations is classified to determine intents for each user utterance, and for each of the conversations, a control flow diagram showing the intents and sequential flow of the conversation is generated. Each of the control flow diagrams is processed to generate a graph embedding representative of the conversation. A previous conversation that is similar to the current conversation is identified from a previous graph embedding that is nearest to a current graph embedding of a most recent utterance in a current conversation. A previous outcome of the previous conversation is used to predict an outcome of the current conversation, which, when not positive, may control response outputs of the natural language interface to steer the current conversation towards a positive result.

Abstract: Methods and systems for measuring and predicting content dissemination in social networks includes computing a “virality score” for popularity of social media content, a “pattern” of diffusion of the content, and a “hype” parameter of such content without requiring a “friendship graph” or “information diffusion” structured data. It also includes an iterative, predictive model, which predicts future performance (or future rate of dissemination) of the content while mitigating a class imbalance problem inherent in predicting viral posts, and which provides updates to the model based on actual performance results.