Title: Integrating linguistic knowledge into DNNs: Application to online grooming detection. Abstract: Online grooming (OG) of children is a pervasive issue in an increasingly interconnected world. We explore various complementary methods to incorporate Corpus Linguistics (CL) knowledge into accurate and interpretable Deep Learning (DL) models. They provide an implicit text normalisation that adapts embedding spaces to the groomers' usage of language, and they focus the DNN's attention onto the expressions of OG strategies. We apply these integration to two architecture types and improve on the state-of-the-art on a new OG corpus.