1. A computer implemented method of deriving at least one structured event object from a stream of unstructured text associated with a plurality of sparsely reported events, comprising: detecting one or more potential events in steps of: a) filtering the unstructured text following a specific pattern using a regular expression filter, and b) classifying, using a supervised classifier, the filtered text based upon a set of recognized features; computing a textual similarity score for incoming text stream by comparing each of the incoming text stream with potentially matching neighboring text stream for textual similarity, and dynamically creating a bucket for the incoming text stream upon determining lowest hash values for the incoming text stream and identifying unique combination of the lowest hash values, wherein the potentially matching neighboring text stream corresponds to text streams sharing dynamically created bucket; establishing a correlation, whenever the textual similarity score is above a predetermined threshold score for obtaining one or more partial events that correspond to text streams in the created bucket; extracting conceptual and other related information from the correlated partial events and associated metadata; and deriving the at least one structured event object by establishing a correlation between the partial events based on the extracted conceptual and other information of the corresponding partial events.
1. A computer implemented method of deriving at least one structured event object from a stream of unstructured text associated with a plurality of sparsely reported events, comprising: detecting one or more potential events in steps of: a) filtering the unstructured text following a specific pattern using a regular expression filter, and b) classifying, using a supervised classifier, the filtered text based upon a set of recognized features; computing a textual similarity score for incoming text stream by comparing each of the incoming text stream with potentially matching neighboring text stream for textual similarity, and dynamically creating a bucket for the incoming text stream upon determining lowest hash values for the incoming text stream and identifying unique combination of the lowest hash values, wherein the potentially matching neighboring text stream corresponds to text streams sharing dynamically created bucket; establishing a correlation, whenever the textual similarity score is above a predetermined threshold score for obtaining one or more partial events that correspond to text streams in the created bucket; extracting conceptual and other related information from the correlated partial events and associated metadata; and deriving the at least one structured event object by establishing a correlation between the partial events based on the extracted conceptual and other information of the corresponding partial events. 4. The method of claim 1 , wherein the correlation between the classified texts is established by Locality Sensitive Hashing technique.