1. A machine-implemented method, comprising: requesting a plurality of users to generate human-generated user requests for each of a plurality of task categories to collect a plurality of the human-generated user requests to create a training database of user requests, each user request corresponding uniquely to one of the task categories; for each task category, extracting a training feature vector from each corresponding user request in the training database by assigning a numeric value to each different word in the corresponding user request to form a training data set having a plurality of training feature vectors for each task category; for each task category, processing the training feature vectors in the training data set corresponding to the task category to determine a task classifier model for the 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 identify which task category corresponds to the additional request.