The network was trained for 15 epoches on 58 documents (longer training did not necessarily result in a better classification accuracy) and applied to a separate set spanning 14 documents. This second set is then used to train the second network, using Bi-LSTM character embeddings with a subsequent Bi-LSTM. However, the network is only applied to tokens that had been classified as a character reference in the first pass. This follows the intuition that it can now be decided if the current instance is of a different semantic category, which can be detected by analyzing its context. The input of this network is the snippet around the target word with a context size of two. The second task of this network, detecting the exact bounds of a reference, is done by predicting labels in an I-O-B setting. It is noteworthy that words that were not detected in the first pass can not be recovered. The second network is trained with 25 epochs and finally tested on 18 test documents.