Sebastian Moller's scientific contributions

Publication (1)

Conference Paper
Tools and resources to automatically process clinical text are very limited, particularly outside the English speaking world. As many relevant patient information within electronic health records are described in unstructured text, this is a clear drawback. In order to slightly overcome this problem, we present information extraction models for Ger...

Citations

... First we ran an automatic annotation. This included the partial automatic labelling of the semantic layer using mEx (Roller et al., 2020) for named entity recognition and relation extraction. Although the schema of mEx partially differs, it includes various of our entities and is a good start to speed up the annotation. ...