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cs.CL2024
Assisted Data Annotation for Business Process Information Extraction from Textual Documents
Julian Neuberger, Han van der Aa, Lars Ackermann +3
Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase…
cs.CL2024
A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models
Julian Neuberger, Lars Ackermann, Han van der Aa +1
Over the past decade, extensive research efforts have been dedicated to the extraction of information from textual process descriptions. Despite the remarkable progress witnessed i…
cs.CL2024
Leveraging Data Augmentation for Process Information Extraction
Julian Neuberger, Leonie Doll, Benedict Engelmann +2
Business Process Modeling projects often require formal process models as a central component. High costs associated with the creation of such formal process models motivated many…