Learning information intent via observation

Users in an organization frequently request help by sending request messages to assistants that express information intent: an intention to update data in an information system. Human assistants spend a significant amount of time and effort processing these requests. For example, human resource assistants process requests to update personnel records, and executive assistants process requests to schedule conference rooms or to make travel reservations. To process the intent of a request, assistants read the request and then locate, complete, and submit a form that corresponds to the expressed intent. Automatically or semi-automatically processing the intent expressed in a request on behalf of an assistant would ease the mundane and repetitive nature of this kind of work.For a well-understood domain, a straightforward application of natural language processing techniques can be used to build an intelligent form interface to semi-automatically process information intent request messages. However, high performance parsers are based on machine learning algorithms that require a large corpus of examples that have been labeled by an expert. The generation of a labeled corpus of requests is a major barrier to the construction of a parser. In this paper, we investigate the construction of a natural language processing system and an intelligent form system that observes an assistant processing requests. The intelligent form system then generates a labeled training corpus by interpreting the observations. This paper reports on the measurement of the performance of the machine learning algorithms based on real data. The combination of observations, machine learning and interaction design produces an effective intelligent form interface based on natural language processing.