A Proposal for Postpartum Support Based on Natural Language Generation Model

This article presents a Multifaceted Natural Language Generation Model to help women in a postpartum. This proposal supports speech and text interactions, developing responses through an ontology model and retrieval based-method techniques oriented to the welfare of women after the birth of their baby. We show a case study, introducing an architecture model of conversational agents, a scenario and a simulation of this proposal. Our results presented a better performance in response time generated from a retrieval based-method(two seconds), and similar response accuracy, slightly better for semantic queries (20% better).