The lack of specific data sets makes difficult the discourse parsing for Informal Mathematical Discourse (IMD). In this paper, we propose a data driven approach to identify arguments and connectives in an IMD structure within the context of Controlled Natural Language (CNL). Our approach follows a low-level discourse parsing under Peen Discourse TreeBank (PDTB) guidelines. Three classifiers have been trained: one that identifies the
, other that locates the relative position of
and a third that identifies the (
) arguments of each connective. These classifiers are instances of Support Vector Machines (SVMs), fed from an own Mathematical TreeBank. Finally, our approach defines an End-to-End discourse parser into IMD, whose results will be used to classify of informal deductive proofs via the low level discourse in IMD processing.