5. A method for use with a set of seed documents extracted from a data warehouse, the method comprising: searching the data warehouse to provide a set of additional documents similar to the set of seed documents, wherein said similarity is determined using a statistical method; generating an initial taxonomy for a combined document set that includes all documents from both the set of seed documents and the set of additional documents generating categories for the initial taxonomy wherein said generating of categories includes generation of a feature space from text of the set of seed documents and comparison of the set of seed documents using a measure of distance in the feature space; iterating the processes of extracting, searching, and generating by the performance of a computer processor, and using domain knowledge to produce a refined taxonomy from the initial taxonomy; refining the taxonomy by creating new categories in the taxonomy based on a relationship between the categories in the taxonomy; classifying the combined document set using structured fields; using contingency analysis to generate a contingency table that compares categories of the refined taxonomy to the structured fields; calculating an expected value percentage for each of a plurality of cells in the contingency table as the expected percent of an author's documents out of a total number for one of the categories, multiplied by a percent of documents in the one of the categories out of a total number of documents for all categories; determining a statistical relationship between the author and one of the categories of the refined taxonomy in the table; and shading each of the plurality of cells in the contingency table based on the degree to which the expected value percentage exceeds an actual value in each of the plurality of cells in the contingency table.
9. The method of claim 5 , further comprising: wherein in the classifying of the combined document set using structured fields, at least one structured field includes names of people; examining the contingency table to find a relationship between the categories and the names of people; and further refining the refined taxonomy by filtering out noise using user domain knowledge.