Abstract: System, method, and computer program product to identify candidate values to provide to a deep question answering (QA) system as part of a case, by receiving a case, wherein the case includes a plurality of documents for evaluation by the deep QA system, evaluating the plurality of documents using natural language processing (NLP) to identify one or more concepts reflected by text content within the plurality of documents in the case, wherein the plurality of documents includes a plurality of distinct values for at least a first one of the concepts, selecting, from the plurality of distinct values, a candidate value for the first concept to provide to the deep QA system to process the case, and prior to submitting the case to the deep QA system, returning at least the candidate value selected for the first concept to present in a user interface.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: System, method, and computer program product to identify candidate values to provide to a deep question answering (QA) system as part of a case, by receiving a case, wherein the case includes a plurality of documents for evaluation by the deep QA system, evaluating the plurality of documents using natural language processing (NLP) to identify one or more concepts reflected by text content within the plurality of documents in the case, wherein the plurality of documents includes a plurality of distinct values for at least a first one of the concepts, selecting, from the plurality of distinct values, a candidate value for the first concept to provide to the deep QA system to process the case, and prior to submitting the case to the deep QA system, returning at least the candidate value selected for the first concept to present in a user interface.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: Method, system, and computer program product to analyze a plurality of candidate answers identified as responsive to a question presented to a deep question answering system, by computing a first feature score for a first feature of an item of evidence, of a plurality of items of evidence, the first feature score being based on at least one attribute of the first feature, the item of evidence relating to a first candidate answer, of the plurality of candidate answers, and computing a merged feature score for the first candidate answer by applying the first feature score to a second feature score for a second feature of the item of evidence.

Abstract: The preferred embodiment of the present invention provides system for data collection that provides a solution that can be easily extended to collect different data types. The preferred embodiment provides a collection repository that stores different collected data types with a single consistent interface for accessing the collected data. The preferred embodiment is a data collector implemented from an object-oriented framework. The object-oriented data collector can be easily extended by adding additional instances of the data collector from the framework. The preferred embodiment framework includes mechanisms for global customization of the collector and synchronization of the collection. The preferred embodiment also includes base probe classes that define the probes that perform the actual collection of the system data and store the data in a common collection repository. Subclasses are provided to implement the collection of different types of system data.

Abstract: Computer systems have the ability to monitor their components and operations, generate events which indicate the occurrence of a monitored condition (e.g. out of paper, Joe Smith just signed on, disk utilization nearing capacity, etc.), and process these events in some manner. The present invention provides significant enhancements to the latter capability.The events of an event stream or streams are "filtered" into categories or groups of events. Once categorized, the invention associates an action or actions with the categorized event. The associated action can be logging the event, routing the event to the electronic address of a user, or sending the event to an application program for further processing.