Patent ID: 8856123

Claim:
A method comprising: for a given category, receiving a positive set of training documents within the given category and a negative set of training documents not within the given category, by a processor of a computing device; using a feature selector on the positive set and the negative set to determine a first set of features that are predictive for the given category, by the processor, each feature comprising a word or a phrase of words; training a first classifier from the positive set and the negative set to assign a weight to each feature of the first set of features, by the processor; after training the first classifier, querying a document index of a plurality of production documents for each feature of the first set of features to yield a sub-plurality of the production documents that are likely but not necessarily within the given category, by the processor, by formulating a query that includes the first set of features as weighted by the weights thereof to locate the sub-plurality of the production documents, where each of the sub-plurality of the production documents resulting from querying the document index has a total sum of the weights of the first set of features greater than a threshold; and applying a second classifier that uses a second set of features greater in number than the first set to determine whether each production document of the sub-plurality is predicted to be within the given category, by the processor, wherein the document index is queried to decrease a number of the production documents against which the second classifier is applied to just the sub-plurality of the production documents yielded by querying the document index, and wherein the first classifier is one of a Naïve Bayesian classifier, a support vector machine classifier, and a logistic regression classifier.