However, all of these approaches classify each sentence and every token inside on their own so that subsequent sentences do not benefit from previously detected names. This does not only overcomplicate the detection, it can also introduce inconsistent results (e.g. “Effi” is detected as a name in sentence 10 and 27, but was not detected in sentence 25). Introducing no dependencies between each individual reference seems a wasted opportunity, especially in novels, because the same character reappears multiple times. This paper experimented with network architectures to leverage this shortcoming. The idea of this approach is not new and can even be dated back to the Brills Tagger (Brill 1992) - the classification by two separate classifiers each with its individual perspective on the problem.