Despite the significant progress made in Natural Language Processing (NLP) thanks to deep learning techniques, efforts are still needed to model explicit, factual, and accurate meaning representation formalisms. In this article, we present a comparative table of ten formalisms that have been proposed over the last thirty years, and we describe and put forth our own, Meaning Representation for Application Purposes (MR4AP), developed in an industrial context with a definitive applicative aim.