A mechanism is provided in a data processing system for categorizing a user providing a text input. The mechanism receives an input text written by a user and determines a set of features associated with the input text. The mechanism processes the input text and the set of features by a detection model. The detection model comprises a plurality of detectors corresponding to a plurality of categories. Each of the plurality of detectors determines whether the user fits a respective category based on the input text and the set of features. The mechanism categorizes the user into one or more of the plurality of categories based on a result of processing the input text and the set of features by the detection model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for categorizing a user providing a text input, the method comprising: receiving, by the data processing system, an input text written by a user; determining, by the data processing system, a set of features associated with the input text; processing, by the data processing system, the input text and the set of features by a detection model, wherein the detection model comprises a plurality of detectors corresponding to a plurality of categories, wherein each of the plurality of detectors determines whether the user fits a respective category based on the input text and the set of features; and categorizing, by the data processing system, the user into one or more of the plurality of categories based on a result of processing the input text and the set of features by the detection model. 2. The method of claim 1, wherein each detector determines a presence probability that the user fits the respective category based on the input and the set of features, determines an absence probability that the user fits a default category based on the input and the set of features, and compares a difference between the presence probability and the absence probability to a threshold. 3. The method of claim 2, wherein determining the presence probability for an ith category comprises calculating a first discriminant function as follows: p(X|θ0(i)), where X represents the set of features associated with the input text and θ0(i) represents features that fit the ith category. 4. The method of claim 3, wherein determining the absence probability for the ith category comprises calculating a second discriminant function as follows: p(X|θ1(i)), where θ0(i) represents features that do not fit the ith category. 5. The method of claim 1, wherein the detection model is a parallel detection model, wherein the plurality of detectors determine whether the user fits their respective categories in parallel. 6. The method of claim 1, wherein the detection model is a series detection model, wherein the plurality of detectors determine whether the user fits their respective categories in series. 7. The method of claim 6, wherein the detection model ends processing responsive to a detector determining the user fits a respective category. 8. The method of claim 7, wherein the detection model determines the user fits a default category responsive to no detectors determining the user fits a respective category. 9. The method of claim 1, wherein the plurality of categories comprise a plurality of distinct categories. 10. The method of claim 1, wherein the plurality of categories comprise a plurality of levels of a user characteristic. 11. The method of claim 10, wherein the plurality of levels of the user characteristic comprise one of the following: a plurality of expertise levels; a plurality of levels of language fluency; a plurality of degrees of urgency; or a plurality of degrees of user frustration. 12. The method of claim 1, wherein determining the set of features associated with the input text comprises: extracting a plurality of features from the input text using an annotation engine pipeline. 13. The method of claim 12, wherein determining the set of features associated with the input text further comprises: obtaining features from the user's posting history within a collection of question and answer postings. 14. The method of claim 13, wherein determining the set of features associated with the input text further comprises: obtaining features from responses by other users within the collection of question and answer postings. 15. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive an input text written by a user; determine a set of features associated with the input text; process the input text and the set of features by a detection model, wherein the detection model comprises a plurality of detectors corresponding to a plurality of categories, wherein each of the plurality of detectors determines whether the user fits a respective category based on the input text and the set of features; and categorize the user into one or more of the plurality of categories based on a result of processing the input text and the set of features by the detection model. 16. The computer program product of claim 15, wherein each detector determines a presence probability that the user fits the respective category based on the input and the set of features, determines an absence probability that the user fits a default category based on the input and the set of features, and compares a difference between the presence probability and the absence probability to a threshold. 17. The computer program product of claim 15, wherein the detection model is a parallel detection model, wherein the plurality of detectors determine whether the user fits their respective categories in parallel. 18. The computer program product of claim 15, wherein the detection model is a series detection model, wherein the plurality of detectors determine whether the user fits their respective categories in series. 19. The computer program product of claim 18, wherein the detection model ends processing responsive to a detector determining the user fits a respective category. 20. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive an input text written by a user; determine a set of features associated with the input text; process the input text and the set of features by a detection model, wherein the detection model comprises a plurality of detectors corresponding to a plurality of categories, wherein each of the plurality of detectors determines whether the user fits a respective category based on the input text and the set of features; and categorize the user into one or more of the plurality of categories based on a result of processing the input text and the set of features by the detection model.