document: 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 .
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