Our first contribution is therefore a deep learning-based method to detect direct speech using large amounts of rule-based, but slightly flawed, labelled data extracted from raw text. This has multiple advantages over the use of manually annotated training data: First, manually annotating large amounts of text is very time-intensive and therefore costly. Furthermore, annotations for one type of texts may not be transferable to other types, leading to the necessity of new annotated data for new corpora. Being able to learn from the already existing weakly labelled data is therefore desirable, as this data can automatically be extracted for a new corpus.