In her study about the automatic recognition of speech representation in German literary texts, Brunner 2015 implements two strategies for DS detection: Her rule-based approach uses quotation marks as well as pattern matching to identify frames (proper name - speech verb - colon/comma). This leads to some success in texts with unmarked DS. On a corpus of 13 German narrative texts an f-score of 0.84 for the category <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic" xml:space="preserve">direct </hi>is achieved in a sentence-wise evaluation. Brunner also implements a machine learning approach using random forests and features based on POS-tags and markings for typical speech/thought/writing words. In a ten-fold cross validation, this model achieves an f-score of 0.87 for <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">direct</hi>. If quotation marks are ignored entirely in training and evaluation, the model still achieves an f-score of 0.81.