Usagers & Recherche d'Information

La recherche d'information est confrontée à une variété de plus en plus importante tant en termes d'usagers, de tâches à remplir, d'outils.... Face à cette hétérogénéité de nombreux travaux, s'attachent à améliorer la recherche d'information par le biais d'approches adaptatives, de systèmes de recommandation... Mes travaux s'inscrivent dans ce cadre et apportent un éclairage essentiellement porté sur l'usager et ses activités et plus particulièrement sur la recherche d'information. Les résultats correspondent à 3 angles d'investigation nous permettant d'aborder cette problématique de l'hétérogénéité en Recherche d'Information

WWW 2007 / Track: Browsers and User Interfaces Session: Smarter Browsing Learning Information Intent via Observation

Workers in organizations frequently request help from assistants by sending request messages 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, an assistant reads the request and then locates, completes, and submits a form that corresponds to the expressed intent. Automatically or semiautomatically 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 machinelearning 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