Process Extraction from Natural Language Text: the PET Dataset and Annotation Guidelines

Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists very little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work, we aim to fill this gap and establish the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization aimed at Business Process Management. For this, we develop the first corpus of business process descriptions annotated with activities, gateways, actors, and flow information. We present our new resource, including a detailed overview of the annotation schema and guidelines, as well as a variety of baselines to benchmark the difficulty and challenges of business process extraction from text.