25. The method as recited in claim 1 , wherein the step of selecting further comprises the steps of: determining criteria for a size for a keyword collection; whenever the criteria specify allowing the machine learning algorithm to decide which candidate entries will be selected for the keyword collection, retaining all candidate entries that are selected by the machine learning algorithm; and whenever the criteria specify using the numeric scores to select the entries to be retained, then whenever the criteria specify using a cutoff score, retaining all candidate entries having a score higher than the cutoff score, and whenever the criteria specify using a size for the keyword collection that is a predetermined ratio of the size of the electronic document, retaining a fraction of the candidate entries wherein the retained fraction of entries comprises those candidate entries having the highest numeric scores and results in the specified size for the keyword collection.
1. A method for automatically generating one or more keywords from an electronic document, the method comprising the steps of: identifying candidate entries for the keywords by extracting all n-grams up to a specified length that do not cross sentence boundaries, reducing a size of a data set consisting of the extracted n-grams by applying one or more filters and balancing a distribution of positive and negative examples whenever the data set is derived from a training data set; constructing a feature vector for each candidate entry, wherein the feature vector comprises at least one feature selected from the group consisting of one or more discourse comprehension features, one or more part-of-speech pattern features, and one or more encyclopedic annotation features using a computer; assigning a numeric score to each candidate entry based on the feature vector for that candidate entry using a computer; and selecting a specified number of candidate entries to be retained as the keywords.