1. A machine-implemented method, comprising: collecting a plurality of human-generated requests for each of a plurality of task categories to create a training database of user requests; extracting a training feature vector from each user request in the training database by assigning a binary value to each different word in the user request to form a training data set having a plurality of training feature vectors for each task category; processing the training feature vectors in the training data set to determine a task classifier model for each task category; receiving an additional request from a user, the additional request being classifiable into one of the task categories; and in the machine, comparing an extracted feature vector from the additional request to the task classifier model to determine a predicted task category for the additional request.