The process of identifying obligations in a legal text is not a straightforward task, because not only are the documents long, but the sentences therein are long as well. As a result of long elements in the text, law is more difficult to interpret (Coupette et al., 2021). Moreover, the identification of obligations relies not only on the clarity and precision of the language used but also on the unique perspectives, experiences, and knowledge of the reader. In particular, this paper addresses the problem of identifyingobligations using machine and deep learning approaches showing a full comparison between both methodologies and proposing a new approach called NOMOS based on the combination of Positional Embeddings (PE) and Temporal Convolutional Networks (TCNs). Quantitative and qualitative experiments, conducted on legal regulations 1, demonstrate the effectiveness of the proposed approach.