This section only mentions the most dominant work in terms of Named Entity Recognition techniques as well as those with comparable results in terms of domain. The standard approach is to segment the input document into sentences and apply a sequence classifier on these sentences. The most robust classifier applied to this task was a Conditional Random Field (CRF) (Lafferty et al. 2001). Until the success of deep learning approaches, the classifier published by Stanford (Finkel et al. 2005) and its adaptation to German (Faruqui et al. 2010) were the dominant approaches. Lately the combination of a Bi-LSTM with word-embeddings (e.g. Mikolov et al. 2013) which automatically derived features for a CRF classifier in a deep learning approach surpassed manually created features (Huang et al. 2015). Most recently, Riedl and Padó (2018) included pretraining into the Bi-LSTM-CRF architecture and achieved state of the art results. A maximum entropy classifier with cluster features derived by word2vec and manually crafted features yielded an F1-score of 90% (Jannidis et al. 2017), serving as the only comparable work for German literary data.