1. A system for determining document classification probabilistically through classification rule analysis, the system comprising: at least one server comprising: a memory configured to store instructions; and a processor configured to execute an application in conjunction with the instructions stored in the memory, wherein the application is configured to: identify patterns and evidences within content of representative documents; construct a classification rule based on an entity determined according to an analysis of the patterns and an affinity determined according to an analysis of the evidences; and process the content with the classification rule to: determine an entity count and an entity confidence level for the entity; determine an affinity presence and an affinity confidence level for the affinity, wherein the affinity confidence level is determined from a probability of at least one of the evidences being within a proximity window of a presence of the affinity, and the proximity window includes a window of the content used to scan the content for the affinity; aggregate the entity count, the entity confidence level, the affinity presence, and the affinity confidence level to returned results; compare the returned results to expected results to evaluate the classification rule against acceptance requirements; and in response to a determination that the classification rule meets the acceptance requirements, identify confidence levels for the patterns and the evidences; else edit the classification rule.
1. A system for determining document classification probabilistically through classification rule analysis, the system comprising: at least one server comprising: a memory configured to store instructions; and a processor configured to execute an application in conjunction with the instructions stored in the memory, wherein the application is configured to: identify patterns and evidences within content of representative documents; construct a classification rule based on an entity determined according to an analysis of the patterns and an affinity determined according to an analysis of the evidences; and process the content with the classification rule to: determine an entity count and an entity confidence level for the entity; determine an affinity presence and an affinity confidence level for the affinity, wherein the affinity confidence level is determined from a probability of at least one of the evidences being within a proximity window of a presence of the affinity, and the proximity window includes a window of the content used to scan the content for the affinity; aggregate the entity count, the entity confidence level, the affinity presence, and the affinity confidence level to returned results; compare the returned results to expected results to evaluate the classification rule against acceptance requirements; and in response to a determination that the classification rule meets the acceptance requirements, identify confidence levels for the patterns and the evidences; else edit the classification rule. 2. The system of claim 1 , wherein the application is further configured to: in response to a determination that an entity rule meets acceptance requirements, process the entity rule against another document, wherein the acceptance requirements include precision requirements for established thresholds and recall rates for established thresholds.