Approaches presented herein enable intelligent service request classification and assignment learning. More specifically, a request comprising a free form text or spoken description is received from a user. The request description is parsed and classified by a regression-based classifier. The regression-based classifier classifies based on, for example: the description itself; the requestor's history of requests, and/or supplemental demographics about a requestor. Optionally, a user may verify the classification or select from a plurality of returned classifications. A service provider or administrator confirms that a classification is correct. If not, the incorrectly classified request is queued. If so, the correctly classified request is added to a set of training data to be used in classifying future requests.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of intelligent service request classification and assignment learning, comprising: receiving a single description of a service request from a user; applying to the service request description a logistic regression algorithm configured to determine a classification of the service request based on the description and a historic frequency of classified service requests submitted by the user; receiving a determination from a human administrator whether the service request is correctly classified; and continuously training the logistic regression algorithm against an increasingly refined set of training data of correctly classified service requests. 2. The method of claim 1, wherein the logistic regression algorithm is a naïve Bayes classifier. 3. The method of claim 1, where, in the case that the service request is correctly classified, the method further comprises storing an identification of the user and a classification of the service request in the set of training data. 4. The method of claim 3, further comprising: calculating a frequency the user requests a service of the classification; and storing the frequency in the set of training data. 5. The method of claim 1, further comprising: predicting a probability a classification determined with the logistic regression algorithm applies to the service request; and in the case that the probability is within a threshold, assigning the classification to the service request automatically. 6. The method of claim 5, where, in the case that the probability is under a threshold, the method further comprises: returning a set of one or more classification options to the user; and assigning a classification option selected by the user to the service request. 7. The method of claim 1, wherein the logistic regression algorithm is further configured to determine a classification of the service request based on supplemental demographics about the user, the supplemental demographics being collected from one or more of: a human resources (HR) system, a configuration management database (CMDB), a Customer Relationship Management (CRM) system, an Enterprise Resource Planning (ERP) system, and a social media system. 8. A computer system for intelligent service request classification and assignment learning, the computer system comprising: a memory device comprising program instructions; a bus coupled to the memory device; and a processor, for executing the program instructions, coupled to a request management tool via the bus that when executing the program instructions causes the system to: receive a single description of a service request from a user; apply to the service request description a logistic regression algorithm configured to determine a classification of the service request based on the description and a historic frequency of classified service requests submitted by the user; receive a determination from a human administrator whether the service request is correctly classified; and continuously train the logistic regression algorithm against an increasingly refined set of training data of correctly classified service requests. 9. The system of claim 8, wherein the logistic regression algorithm is a naïve Bayes classifier. 10. The system of claim 8, where, in the case that the service request is correctly classified, the program instructions further cause the system to store an identification of the user and a classification of the service request in the set of training data. 11. The system of claim 10, the program instructions further causing the system to: calculate a frequency the user requests a service of the classification; and store the frequency in the set of training data. 12. The system of claim 8, the program instructions further causing the system to: predict a probability a classification determined with the logistic regression algorithm applies to the service request; and in the case that the probability is within a threshold, assign the classification to the service request automatically. 13. The system of claim 12, where, in the case that the probability is under a threshold, the program instructions further cause the system to: return a set of one or more classification options to the user; and assign a classification option selected by the user to the service request. 14. The system of claim 8, wherein the logistic regression algorithm is further configured to determine a classification of the service request based on supplemental demographics about the user, the supplemental demographics being collected from one or more of: a human resources (HR) system, a configuration management database (CMDB), a Customer Relationship Management (CRM) system, an Enterprise Resource Planning (ERP) system, and a social media system. 15. A computer program product for intelligent service request classification and assignment learning, the computer program product comprising a computer readable storage device, and program instructions stored on the computer readable storage device, to receive a single description of a service request from a user; apply to the service request description a logistic regression algorithm configured to determine a classification of the service request based on the description, a historic frequency of classified service requests submitted by the user, and supplemental demographics about the user; receive a determination from a human administrator whether the service request is correctly classified; and continuously train the logistic regression algorithm against an increasingly refined set of training data of correctly classified service requests. 16. The computer program product of claim 15, wherein the logistic regression algorithm is a naïve Bayes classifier. 17. The computer program product of claim 15, where, in the case that the service request is correctly classified, the program instructions are further configured to store an identification of the user and a classification of the service request in the set of training data. 18. The computer program product of claim 17, the program instructions being further configured to: calculate a frequency the user requests a service of the classification; and store the frequency in the set of training data. 19. The computer program product of claim 15, the program instructions being further configured to: predict a probability a classification determined with the logistic regression algorithm applies to the service request; and in the case that the probability is within a threshold, assign the classification to the service request automatically. 20. The computer program product of claim 19, where, in the case that the probability is under a threshold, the program instructions are further configured to: return a set of one or more classification options to the user; and assign a classification option selected by the user to the service request.