Romberg, J. (2023). Mind the User! Measures to More Accurately Evaluate the Practical Value of Active Learning Strategies. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing (pp. 996-1006), Varna, Bulgaria. INCOMA Ltd.

Romberg, J. (2023). Unterstützung politischer Entscheidungen durch KI-gestützte Auswertung von Bürger:innenbeteiligungsverfahren. In Quo Vadis Künstliche Intelligenz in der nuklearen Entsorgung? Eine Sammlung transdisziplinärer Perspektiven (pp. 14-16). Bundesamt für die Sicherheit der nuklearen Entsorgung.

Romberg, J., & Escher, T. (2023). Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public Participation. Digital Government: Research and Practice. Just Accepted.

Romberg, J. (2022). Is Your Perspective Also My Perspective? Enriching Prediction with Subjectivity. In Proceedings of the 9th Workshop on Argument Mining (pp.115-125), Gyeongju, Republic of Korea. Association for Computational Linguistics.

Romberg, J., & Escher, T. (2022). Automated Topic Categorisation of Citizens’ Contributions: Reducing Manual Labelling Efforts Through Active Learning. In: Marijn Janssen, et al. Electronic Government. EGOV 2022. Lecture Notes in Computer Science, vol 13391. Springer, Cham.

Romberg, J., Mark, L., & Escher, T. (2022). A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification. In Proceedings of the Language Resources and Evaluation Conference (pp. 2874–2883), Marseille, France. European Language Resources Association.

Romberg, J., & Conrad, S. (2021). Citizen Involvement in Urban Planning – How Can Municipalities Be Supported in Evaluating Public Participation Processes for Mobility Transitions?. In Proceedings of the 8th Workshop on Argument Mining (pp. 89-99), Punta Cana, Dominican Republic. Association for Computational Linguistics.

Romberg, J., Dyczmons, J., Borgmann, S. O., Sommer, J., Vomhof, M., Brunoni, C., Bruck-Ramisch, I., Enders, L., Icks, A., & Conrad, S. (2020). Patient Information Needs in Online Diabetes Forums. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task (pp. 19-26), Barcelona, Spain (Online). Association for Computational Linguistics.

Oberstrass, A., Romberg, J., Stoll, A., & Conrad, S. (2019). HHU at SemEval-2019 Task 6: Context does matter-tackling offensive language identification and categorization with ELMo. In Proceedings of the 13th International Workshop on Semantic Evaluation (pp. 628-634), Minneapolis, Minnesota, USA. Association for Computational Linguistics.

Romberg, J. (2018). GDWDS: First Insights from a Student-based Key Phrase Annotation Process of Medical Information Needs on a Novel German Diabetes Web Data Set. In Proceedings of the 30th GI-Workshop on Foundations of Databases (pp. 89-94). CEUR Workshop Proceedings.

Cabanski, T., Romberg, J., & Conrad, S. (2017). HHU at SemEval-2017 Task 5: Fine-grained Sentiment Analysis on Financial Data using Machine Learning Methods. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017) (pp. 832-836), Vancouver, Canada. Association for Computational Linguistics.

Romberg, J. (2017). Comparing Relevance Feedback Techniques on German News Articles. Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband.

Romberg, J. (2016). Actor Identification and Relevance Filtering in Movie Reviews. In Proceedings of the 28th GI-Workshop on Foundations of Databases (pp. 92–97). CEUR Workshop Proceedings.