7. An event extraction system comprising: an event concept store that comprises a non-transitory machine-readable memory storing one or more event models, one or more event categorization tables, and an uncategorized event table, wherein each event model of the one or more event models is associated with an event candidate and further comprises a threshold event score and a set of one or more features, wherein each event categorization table comprises one or more known event models, and wherein the at least one uncategorized event table comprises a set of one or more features, a set of one or more entities, and a set of one or more topics, each associated with one or more uncategorized event models; and an event category validation processor communicatively coupled to a network and executes an event extractor module configured to: receive a set of extracted features, a set of extracted entities, and a set of extracted topics from a normalized pre-processed data stream associated with a data source, wherein at least one feature of the set of extracted features, the set of extracted entities, and the set of extracted topics is an event candidate; assign an initial confidence score to the set of extracted features, the set of extracted entities, and the set of extracted topics; calculate a score using the initial confidence score of the set of extracted features, the set of extracted entities, and the set of extracted topics that repeat themselves or overlap in different data sources; determine that an event candidate occurred when the calculated score is greater than a predetermined threshold; compare each of the sets in which the at least one feature is the event candidate with the one or more event categorization tables to determine whether the extracted features, entities, and topics, correspond to a known event model; and compare each of the sets with the uncategorized event table to determine whether the extracted features, entities, and topics, correspond with at least one uncategorized event model, wherein the event extractor module is further configured to increase a confidence score corresponding to the at least one uncategorized event model when the set of extracted features are similar to the at least one uncategorized event model in the uncategorized event table, wherein the uncategorized event table store records associated with new unknown event models and a probability score indicating features representing new event models, and wherein the event extractor module is further configured to calculate a probability score indicating features, wherein the probability score is determined based on a likelihood that the features represents new event models; and store the at least one uncategorized event model as a new event model in the one or more event categorization tables responsive to determining the confidence score and the probability score corresponding to the at least one uncategorized event model satisfies a threshold score.
7. An event extraction system comprising: an event concept store that comprises a non-transitory machine-readable memory storing one or more event models, one or more event categorization tables, and an uncategorized event table, wherein each event model of the one or more event models is associated with an event candidate and further comprises a threshold event score and a set of one or more features, wherein each event categorization table comprises one or more known event models, and wherein the at least one uncategorized event table comprises a set of one or more features, a set of one or more entities, and a set of one or more topics, each associated with one or more uncategorized event models; and an event category validation processor communicatively coupled to a network and executes an event extractor module configured to: receive a set of extracted features, a set of extracted entities, and a set of extracted topics from a normalized pre-processed data stream associated with a data source, wherein at least one feature of the set of extracted features, the set of extracted entities, and the set of extracted topics is an event candidate; assign an initial confidence score to the set of extracted features, the set of extracted entities, and the set of extracted topics; calculate a score using the initial confidence score of the set of extracted features, the set of extracted entities, and the set of extracted topics that repeat themselves or overlap in different data sources; determine that an event candidate occurred when the calculated score is greater than a predetermined threshold; compare each of the sets in which the at least one feature is the event candidate with the one or more event categorization tables to determine whether the extracted features, entities, and topics, correspond to a known event model; and compare each of the sets with the uncategorized event table to determine whether the extracted features, entities, and topics, correspond with at least one uncategorized event model, wherein the event extractor module is further configured to increase a confidence score corresponding to the at least one uncategorized event model when the set of extracted features are similar to the at least one uncategorized event model in the uncategorized event table, wherein the uncategorized event table store records associated with new unknown event models and a probability score indicating features representing new event models, and wherein the event extractor module is further configured to calculate a probability score indicating features, wherein the probability score is determined based on a likelihood that the features represents new event models; and store the at least one uncategorized event model as a new event model in the one or more event categorization tables responsive to determining the confidence score and the probability score corresponding to the at least one uncategorized event model satisfies a threshold score. 10. The system according to claim 7 , wherein the set of extracted features are identified in a data stream associated with a data source.