13. A method for performing discovery of digital information in a subject area, comprising: designating through a user interface of a computer a corpus comprising electronically-stored digital information, which are maintained in a storage device; selecting one or more topics and training material for the selected topics comprising on topic information and off topic information; building candidate topic models on the computer comprising: selecting seed words for each of the selected topics; and generating patterns from the seed words for each topic as candidate topic models for that topic; evaluating the candidate topic models for each selected topic against the training material comprising: matching the patterns in each candidate topic model to the training material; rating each candidate topic model for the selected topic comprising: assigning a higher score to each candidate topic model that matches the on topic information for the selected topic; assigning a lower score to each candidate topic model that does not match the on topic information for the selected topic; assigning a higher score to each candidate topic model that does not match the off topic information for the selected topic; and assigning a lower score to each candidate topic model that matches the off topic information for the selected topic; and building an evergreen index comprising topic models for each of the selected topics by pairing each topic to the candidate topic model that has the best overall score.
13. A method for performing discovery of digital information in a subject area, comprising: designating through a user interface of a computer a corpus comprising electronically-stored digital information, which are maintained in a storage device; selecting one or more topics and training material for the selected topics comprising on topic information and off topic information; building candidate topic models on the computer comprising: selecting seed words for each of the selected topics; and generating patterns from the seed words for each topic as candidate topic models for that topic; evaluating the candidate topic models for each selected topic against the training material comprising: matching the patterns in each candidate topic model to the training material; rating each candidate topic model for the selected topic comprising: assigning a higher score to each candidate topic model that matches the on topic information for the selected topic; assigning a lower score to each candidate topic model that does not match the on topic information for the selected topic; assigning a higher score to each candidate topic model that does not match the off topic information for the selected topic; and assigning a lower score to each candidate topic model that matches the off topic information for the selected topic; and building an evergreen index comprising topic models for each of the selected topics by pairing each topic to the candidate topic model that has the best overall score. 17. A method according to claim 13 , further comprising: hierarchically structuring one or more subtopics under at least one of the selected topics.