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Nutrition e-coaches have demonstrated to be a successful tool to foster healthy eating habits, most of these systems are based on graphical user interfaces where users select the meals they have ingested from predefined lists and receive feedback on their diet. On one side the use of conversational interfaces based on natural language processing allows users to interact with the coach more easily and with fewer restrictions. However, on the other side natural language introduces more ambiguity, as instead of selecting the input from a predefined finite list of meals, the user can describe the ingests in many different ways that must be translated by the system into a tractable semantic representation from which to derive the nutritional aspects of interest. In this paper, we present a method that improves state-of-the-art approaches by means of the inclusion of nutritional semantic aspects at different stages during the natural language understanding processing of the user written or spoken input. The outcome generated is a rich nutritional interpretation of each user ingest that is independent of the modality used to interact with the coach.

Introducing Computational Semantics for Natural Language Understanding in Conversational Nutrition Coaches for Healthy Eating