Abstract: VoC data, e.g., plurality of VoC communications, for a plurality of customers can be collected. A clustering algorithm may be utilized to generate a plurality of cluster each of which is assigned a group of customers that share similar metadata. An emotion profile may be generated for each cluster based on segment detected emotions for the VoC data corresponding to the customers that are assigned to the cluster. The emotion profile for a cluster may represent the normal or expected emotions experienced during VoC communications by customers assigned to the cluster. New VoC data corresponding to a new customer may be received. A cluster may be identified based on the metadata of the new customer, and the emotion attributes of the new VoC data may be compared to the emotion profile of the identified cluster. Based on the comparison, one or more deviation can be identified to trigger an action.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a question prediction and answering engine for predicting questions a medical professional is attempting to answer. An interaction monitoring component monitors interaction of a medical professional with a patient electronic medical record (EMR). A question selection component selects a set of questions the medical professional is attempting to obtain an answer to from the patient EMR. The question prediction and answering engine analyzes the patient EMR to generate a set of answers to the set of questions from at least a portion of the patient EMR and outputs a report correlating the set of questions and the set of answers to the medical professional.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a timeline generation system, for automated timeline completion. The timeline generation system executing on the processor of the computing device identifies a plurality of events in documents in a corpus of information. The timeline generation system places the plurality of events in a partial timeline data structure. The timeline generation system selects an event progression from an event progression knowledge base. The timeline generation system aligns the selected event progression to the partial timeline data structure. The timeline generation system identifies a set of events missing from the partial timeline data structure. The timeline generation system maps the set of events missing from the partial timeline data structure to the partial timeline based on the selected event progression to form a completed timeline data structure.

Abstract: A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a question prediction and answering engine for predicting questions a medical professional is attempting to answer. An interaction monitoring component monitors interaction of a medical professional with a patient electronic medical record (EMR). A question selection component selects a set of questions the medical professional is attempting to obtain an answer to from the patient EMR. The question prediction and answering engine analyzes the patient EMR to generate a set of answers to the set of questions from at least a portion of the patient EMR and outputs a report correlating the set of questions and the set of answers to the medical professional.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A contextually relevant patient information extractor is provided that receives an input question directed to medical information about a patient; analyzes a patient's electronic medical records (EMRs) to identify an initial entry in the patient's EMRs corresponding to a candidate answer to the input question; analyzes a context of the patient's EMRs based on the initial entry to identify entries in the patient's EMR that are contextually connected to the initial entry; performs question answering analysis on the initial entry and entries that are contextually connected to the initial entry to identify one or more candidate answers to the input question; and outputs a final answer to the input question based on the question answering analysis.

Abstract: A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a timeline generation system, for automated timeline completion. The timeline generation system executing on the processor of the computing device identifies a plurality of events in documents in a corpus of information. The timeline generation system places the plurality of events in a partial timeline data structure. The timeline generation system selects an event progression from an event progression knowledge base. The timeline generation system aligns the selected event progression to the partial timeline data structure. The timeline generation system identifies a set of events missing from the partial timeline data structure. The timeline generation system maps the set of events missing from the partial timeline data structure to the partial timeline based on the selected event progression to form a completed timeline data structure.