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Process-aware information systems offer extensive advantages to companies,facilitating planning, operations, and optimization of day-to-day businessactivities. However, the time-consuming but required step of designing formalbusiness process models often hampers the potential of these systems. Toovercome this challenge, automated generation of business process models fromnatural language text has emerged as a promising approach to expedite thisstep. Generally two crucial subtasks have to be solved: extractingprocess-relevant information from natural language and creating the actualmodel. Approaches towards the first subtask are rule based methods, highlyoptimized for specific domains, but hard to adapt to related applications. Tosolve this issue, we present an extension to an existing pipeline, to make itentirely data driven. We demonstrate the competitiveness of our improvedpipeline, which not only eliminates the substantial overhead associated withfeature engineering and rule definition, but also enables adaptation todifferent datasets, entity and relation types, and new domains. Additionally,the largest available dataset (PET) for the first subtask, contains noinformation about linguistic references between mentions of entities in theprocess description. Yet, the resolution of these mentions into a single visualelement is essential for high quality process models. We propose an extensionto the PET dataset that incorporates information about linguistic referencesand a corresponding method for resolving them. Finally, we provide a detailedanalysis of the inherent challenges in the dataset at hand.

Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text