  Extracting workflow nets from textual descriptions can be used to simplify
guidelines or formalize textual descriptions of formal processes like business
processes and algorithms. The task of manually extracting processes, however,
requires domain expertise and effort. While automatic process model extraction
is desirable, annotating texts with formalized process models is expensive.
Therefore, there are only a few machine-learning-based extraction approaches.
Rule-based approaches, in turn, require domain specificity to work well and can
rarely distinguish relevant and irrelevant information in textual descriptions.
In this paper, we present GUIDO, a hybrid approach to the process model
extraction task that first, classifies sentences regarding their relevance to
the process model, using a BERT-based sentence classifier, and second, extracts
a process model from the sentences classified as relevant, using dependency
parsing. The presented approach achieves significantly better results than a
pure rule-based approach. GUIDO achieves an average behavioral similarity score
of $0.93$. Still, in comparison to purely machine-learning-based approaches,
the annotation costs stay low.
