EXAMPLE-BASED ITEM CLASSIFICATION

摘要

Item classification rules are created based on examples selected by a user, such as by selecting a subset of emails, and the rule is used across a larger set of items to obtain automatic classification of similar items according to the rule. Based on an analysis, a candidate classification rule is generated identifying text-based features shared among the items of the subset. The user can review the candidate rule as well as a resultant subset of items generated by the rule, and either accept the candidate rule or make an adjustment to the examples and then perform one or more iterations of the analysis to refine the rule. Adjustments can be made by removing items incorrectly included in a resultant subset and/or adding items incorrectly excluded from a resultant subset, and using the adjusted subset in a next iteration.

主权项

1. A method of operating a computerized device to generate a classification rule for use in an application program to selectively classify items of a set of items and display classified items to a user, comprising:
receiving a user selection of an example subset of the set of items; performing an analysis on the example subset to find one or more shared text-based features that are shared across all items of the example subset, and based on the analysis generating a candidate classification rule identifying the shared text-based features; applying the candidate classification rule to the set of items to identify a resultant subset of the items satisfying the candidate classification rule, the resultant subset being a superset of the example subset; displaying the resultant subset to the user and receiving user input indicating, based on user review of the resultant subset, whether the candidate classification rule is accepted; and if the user input indicates that the candidate classification rule is accepted, then finalizing the candidate classification rule into a final classification rule for future use by the application program in classifying items, and otherwise repeating the above steps one or more times for respective adjusted example subsets of the set of items until a respective adjusted candidate classification rule is accepted and finalized into the final classification rule, the adjusted example subset for a given repetition being formed by the user identifying a false match and/or a false non-match with respect to the resultant subset identified in the preceding repetition.