domain performance of six commonly used parsers. Our work is situated in a digital humanities project, in which we are interested in the analysis of various text types such as literature or academic text, for which we do not have sufficient training data available. In our evaluation, we focus on those dependency labels that are most relevant to further analysis for research questions in the humanities. The results show good overall performance with Mate being the most successful parser. In general, however, the performance on some labels of interest to humanist scholars (e. g. non-verbal predicates) is rather poor.