| Data-driven annotation of textual process descriptions based on formal meaning representations L Ackermann, J Neuberger, S Jablonski International Conference on Advanced Information Systems Engineering, 75-90, 2021 | 22 | 2021 |
| Beyond rule-based named entity recognition and relation extraction for process model generation from natural language text J Neuberger, L Ackermann, S Jablonski International Conference on Cooperative Information Systems, 179-197, 2023 | 15 | 2023 |
| Bridging research fields: an empirical study on joint, neural relation extraction techniques L Ackermann, J Neuberger, M Käppel, S Jablonski International Conference on Advanced Information Systems Engineering, 471-486, 2023 | 5 | 2023 |
| Machine allocation via pattern recognition in harmonic waves of manufacturing plants A Reger, J Dumler, O Lobachev, J Neuberger, R Steinhilper Procedia CIRP 67, 69-74, 2018 | 4 | 2018 |
| A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text Using Large Language Models J Neuberger, L Ackermann, H van der Aa, S Jablonski International Conference on Conceptual Modeling, 38-55, 2024 | 2 | 2024 |
| Recent Advances in Data-Driven Business Process Management L Ackermann, M Käppel, L Marcus, L Moder, S Dunzer, M Hornsteiner, ... arXiv preprint arXiv:2406.01786, 2024 | 2 | 2024 |
| Assisted Data Annotation for Business Process Information Extraction from Textual Documents J Neuberger, H van der Aa, L Ackermann, D Buschek, J Herrmann, ... arXiv preprint arXiv:2410.01356, 2024 | 1 | 2024 |
| Leveraging Data Augmentation for Process Information Extraction J Neuberger, L Doll, B Engelmann, L Ackermann, S Jablonski International Conference on Business Process Modeling, Development and …, 2024 | 1 | 2024 |
| TeaPie—A Tool for Efficient Annotation of Process Information Extraction Data J Neuberger, J Herrmann, M Käppel, H van der Aa, S Jablonski | | 2024 |
Stefan JablonskiProfessor of Computer Science, University of Bayreuth, GermanyVerified email at uni-bayreuth.de
