1. A computer implemented method of automatically mapping a given annotator to an aggregate of given annotators, comprising the steps of: providing a plurality of N annotators, denoted Annotator n , for n=1 to N; providing aggregates of the plurality of N annotators, denoted Aggregate i , for i=1-2, . . . 1-N, 2-1 . . . 2-N . . . K-L-M, wherein hyphenated values of the subscript i of Aggregate i represents all of the Annotator n aggregated to form the Aggregate i ; providing an original document corpus; applying a subject annotator to the original document corpus to generate a corpus of subject annotated documents; applying each Annotator n , for n=1 to N, and each Aggregate i , for i=1-2 . . . 1-N, 2-1 . . . 2-N . . . K-L-M, to the original document corpus to generate a corpus of pre-annotated documents; sorting the annotations contained in the pre-annotated documents those annotations meeting a criteria for correlation, in terms of occurrences, to generate good candidate annotations; using the good candidate annotations to decompose the corpus of subject annotated documents into aggregates of the good candidate annotations; after decomposing the subject annotated documents, identifying, using a computer or hardware component, a suitable expression of the subject annotation on a per document basis, wherein suitable expressions are those expressions that have the highest frequencies of occurrence on the given document corpus; and mapping an identified suitable expression to an aggregate of said plurality of N annotators.
1. A computer implemented method of automatically mapping a given annotator to an aggregate of given annotators, comprising the steps of: providing a plurality of N annotators, denoted Annotator n , for n=1 to N; providing aggregates of the plurality of N annotators, denoted Aggregate i , for i=1-2, . . . 1-N, 2-1 . . . 2-N . . . K-L-M, wherein hyphenated values of the subscript i of Aggregate i represents all of the Annotator n aggregated to form the Aggregate i ; providing an original document corpus; applying a subject annotator to the original document corpus to generate a corpus of subject annotated documents; applying each Annotator n , for n=1 to N, and each Aggregate i , for i=1-2 . . . 1-N, 2-1 . . . 2-N . . . K-L-M, to the original document corpus to generate a corpus of pre-annotated documents; sorting the annotations contained in the pre-annotated documents those annotations meeting a criteria for correlation, in terms of occurrences, to generate good candidate annotations; using the good candidate annotations to decompose the corpus of subject annotated documents into aggregates of the good candidate annotations; after decomposing the subject annotated documents, identifying, using a computer or hardware component, a suitable expression of the subject annotation on a per document basis, wherein suitable expressions are those expressions that have the highest frequencies of occurrence on the given document corpus; and mapping an identified suitable expression to an aggregate of said plurality of N annotators. 8. The computer implemented method of claim 1 , wherein the step of mapping is performed to a virtual or actual aggregate of said plurality of N annotators, where “virtual aggregate” means an aggregate that does not exist in the collection of said plurality of N annotators but that may be created if needed, and an “actual aggregate” is one that already exists in the collection of said plurality of N annotators.