Outcome of a natural language interaction

A computer usable program product comprising one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices, the stored program instructions comprising:

• program instructions to train a state prediction model using a plurality of message sequences, each message in a message sequence classified into a message class, the training comprising configuring a Markov inference model to determine a plurality of conditional probabilities, each conditional probability comprising a probability of a message in a second message class following a message in a first message class, wherein a message class is represented by a node in the Markov inference model, the conditional probability is represented by a numerical value assigned to a link between corresponding nodes in the Markov inference model, wherein the training results in a trained state prediction model;
• program instructions to classify, using a natural language analysis, a current message into a current message class, the current message being a portion of an interaction in narrative text form;
• program instructions to forecast, for the interaction using the trained state prediction model, an interaction outcome corresponding to the current message class, the forecasting comprising computing a probability that the current message class will result in a successful message class, wherein the probability that the current message class will result in a successful message class is computed by evaluating a plurality of conditional probabilities corresponding to links connecting nodes in a plurality of paths through the Markov inference model;
• program instructions to determine, using the trained state prediction model, a set of next message classes and a set of predicted interaction outcomes, each message in the set of next message classes corresponding to the current message class, each predicted interaction outcome in the set of predicted interaction outcomes corresponding to a next message class in the set of next message classes, wherein the set of next message classes and the set of predicted interaction outcomes are determined by evaluating the plurality of conditional probabilities in the plurality of paths through the Markov inference model, wherein a predicted interaction outcome in the set of predicted interaction outcomes is determined responsive to a conditional probability of a next message class in the set of next message classes being above a threshold value;
• and
• program instructions to rank, according to the corresponding predicted interaction outcome, the set of next message classes.

A computer system comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising:

• program instructions to train a state prediction model using a plurality of message sequences, each message in a message sequence classified into a message class, the training comprising configuring a Markov inference model to determine a plurality of conditional probabilities, each conditional probability comprising a probability of a message in a second message class following a message in a first message class, wherein a message class is represented by a node in the Markov inference model, the conditional probability is represented by a numerical value assigned to a link between corresponding nodes in the Markov inference model, wherein the training results in a trained state prediction model;
• program instructions to classify, using a natural language analysis, a current message into a current message class, the current message being a portion of an interaction in narrative text form;
• program instructions to forecast, for the interaction using the trained state prediction model, an interaction outcome corresponding to the current message class, the forecasting comprising computing a probability that the current message class will result in a successful message class, wherein the probability that the current message class will result in a successful message class is computed by evaluating a plurality of conditional probabilities corresponding to links connecting nodes in a plurality of paths through the Markov inference model;
• program instructions to determine, using the trained state prediction model, a set of next message classes and a set of predicted interaction outcomes, each message in the set of next message classes corresponding to the current message class, each predicted interaction outcome in the set of predicted interaction outcomes corresponding to a next message class in the set of next message classes, wherein the set of next message classes and the set of predicted interaction outcomes are determined by evaluating the plurality of conditional probabilities in the plurality of paths through the Markov inference model, wherein a predicted interaction outcome in the set of predicted interaction outcomes is determined responsive to a conditional probability of a next message class in the set of next message classes being above a threshold value;
• and
• program instructions to rank, according to the corresponding predicted interaction outcome, the set of next message classes.