We compared the architecture described in Section 4 (denoted 2-stage) with the state of the art architecture (Bi-LSTM-CRF) similar to Riedl and Padó (2018). A Bi-LSTM-CRF using character embeddings in Tensorflow<ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn2" n="2" /> was implemented and applied to the data of DROC. At the current stage our implementation does not make use of pretraining. We used the 90 documents and split them into five folds each comprising 18 different test documents. The remaining 72 documents are used for training. The results are shown in Table 1.