Texts expressed in legal language are often difficult and time consuming for lawyers to read through, particularly for the purpose of identifying relevant deontic modalities (obligations, prohibitions and permissions). By nature, the language of law is strict, hence the predominant use of modal logic as a substitute for the syntactical ambiguity in natural language, specically, deontic and alethic logic for the respective modalities. However, deontic modalities which express obligations,prohibitions and permissions, can have varying degree and preciseness to which they correspond to a matter, strict deontic logic does not allow for such quantitative measures. Therefore, this paper outlines a data-driven approach by classifying deontic modalities using ensembled Articial Neural Networks (ANN) that incorporate domain specic legal distributional semantic model (DSM) representations, in combination with, a general DSM representation. We propose to use well calibrated probability estimates from these classiers as an approximation to the degree which an obligation/prohibition or permission belongs to a given class based on SME annotated sentences. Best results show 82.33 % accuracy on a held-out test set.