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

Abstract

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.

Architecting dietary intake monitoring as a service combining NLP and IoT

“…In the context of nutrition, keyword spotting is very widespread. However, as discussed in our previous work [7] it may generate incomplete interpretations. This is why we proposed to perform a structure analysis in several steps comprising syntactic parsing, entities recognition and semantic analysis, which provides more accurate nutritional interpretations.…”

“…Using a voice or textual interface, users can dictate the list of foods consumed and interact more intuitively [6]. When the user interacts and provides a natural language description of the ingested meal, this description can be processed and decomposed identifying the essential parameters to compute a nutritional record [7].…”

CLASSY: A Conversational Aware Suggestion System

“…Recently, Benítez-Guijarro, Callejas, Noguera, and Benghazi [16] have proposed a different approach, whose goal was to tackle the lack of flexibility regarding keyword extracting techniques. They intended to "improve the current state of the art related to the interaction between nutritional coaching software systems and their users" by introducing a syntactic and semantic analysis of sentences instead of keyword spotting.…”

Coordination of Speech Recognition Devices in Intelligent Environments with Multiple Responsive Devices

“…There also exist studies that present mechanisms to complement the oral interaction with other non-oral sources [10][11][12][13] and also describe some of the challenges of coordinating several speaking objects [4].…”