  Process mining focuses on the analysis of recorded event data in order to
gain insights about the true execution of business processes. While
foundational process mining techniques treat such data as sequences of abstract
events, more advanced techniques depend on the availability of specific kinds
of information, such as resources in organizational mining and business objects
in artifact-centric analysis. However, this information is generally not
readily available, but rather associated with events in an ad hoc manner, often
even as part of unstructured textual attributes. Given the size and complexity
of event logs, this calls for automated support to extract such process
information and, thereby, enable advanced process mining techniques. In this
paper, we present an approach that achieves this through so-called semantic
role labeling of event data. We combine the analysis of textual attribute
values, based on a state-of-the-art language model, with a novel attribute
classification technique. In this manner, our approach extracts information
about up to eight semantic roles per event. We demonstrate the approach's
efficacy through a quantitative evaluation using a broad range of event logs
and demonstrate the usefulness of the extracted information in a case study.
