Data-Driven Annotation of Textual Process Descriptions Based on Formal Meaning Representations. - Lars Ackermann; Julian Neuberger; Stefan Jablonski (CAiSE, 2021)

Bridging Research Fields: An Empirical Study on Joint, Neural Relation Extraction Techniques. - Lars Ackermann; Julian Neuberger; Martin Käppel; Stefan Jablonski (CAiSE, 2023)

Beyond Rule-Based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text. - Julian Neuberger; Lars Ackermann; Stefan Jablonski (CoopIS, 2023)

Leveraging Data Augmentation for Process Information Extraction. - Julian Neuberger; Leonie Doll; Benedikt Engelmann; Lars Ackermann; Stefan Jablonski (BPMDS/EMMSAD@CAiSE, 2024)

Assisted Data Annotation for Business Process Information Extraction from Textual Documents. - Julian Neuberger; Han van der Aa; Lars Ackermann; Daniel Buschek; Jannic Herrmann; Stefan Jablonski (CoopIS, 2024)

TeaPie: A Tool for Efficient Annotation of Process Information Extraction Data. - Julian Neuberger; Jannic Herrmann; Martin Käppel; Han van der Aa; Stefan Jablonski (CoopIS, 2024)

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; Stefan Jablonski (ER, 2024)

Recent Advances in Data-Driven Business Process Management. - Lars Ackermann; Martin Käppel; Laura Marcus; Linda Moder; Sebastian Dunzer; Markus Hornsteiner; Annina Liessmann; Yorck Zisgen; Philip Empl; Lukas-Valentin Herm; Nicolas Neis; Julian Neuberger; et al. (CoRR, 2024)

A Human-in-the-Loop Approach for Improving Fairness in Predictive Business Process Monitoring. - Martin Käppel; Julian Neuberger; Felix Möhrlein; Sven Weinzierl; Martin Matzner; Stefan Jablonski (BPM, 2025)