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Knowledge

Publications

Our publications from research and practice: contributions on NLP, machine learning, privacy, and AI for SMEs, sorted by year.

2026

  1. Berger, U., Biehler, R., Binder, K., Elmer, C., Ertz, F., Hotz, T., Huber, S., Ickstadt, K., Kauermann, G., Küchenhoff, H., Lübke, K., Münnich, R., Schüller, K., Skill, T., Weihs, C., Weinert, H., & Weisser, C. (2026). Daten, Künstliche Intelligenz und Evidenz – neue Anforderungen an die Statistikausbildung an Hochschulen: Diskussion und Erwiderung. AStA Wirtschafts- und Sozialstatistisches Archiv. doi.org/10.1007/s11943-026-00372-0
  2. Kumar, M., Thielmann, A. F., Weisser, C., & Säfken, B. (2026). From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning. Transactions on Machine Learning Research. openreview.net/pdf?id=str7wQt9Qc
  3. Lukassen, F., Herrmann, J., Weisser, C., Silbersdorff, A., Säfken, B., & Kneib, T. (2026). Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids. arXiv. arxiv.org/abs/2605.26770
  4. Lukassen, F., Herrmann, J., Weisser, C., Säfken, B., & Kneib, T. (2026). From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality. arXiv. arxiv.org/abs/2601.02224
  5. Lukassen, F., Weisser, C., Kneib, T., & Silbersdorff, A. (2026). CAFE: A Compound-AI Factorial Evaluation Framework. arXiv. arxiv.org/abs/2607.10380
  6. Lukassen, F., Weisser, C., Schlee, M., Kumar, M., Thielmann, A., Säfken, B., Silbersdorff, A., & Kneib, T. (2026). LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation. arXiv. arxiv.org/abs/2601.02957
  7. Paredes Amorin, A., Python, A., & Weisser, C. (2026). Not All News Is Equal: Topic- and Event-Conditional Sentiment from Finetuned LLMs for Aluminum Price Forecasting. In LREC 2026 – The Fifteenth Biennial Language Resources and Evaluation Conference: Financial Narrative Processing (FNP) Workshop. arxiv.org/abs/2603.09085
  8. Schlee, M., Lukassen, F., & Weisser, C. (2026). LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification. arXiv. arxiv.org/abs/2608.11753
  9. Schlee, M., Weisser, C., Kivimäki, T., Mashiku, M., & Säfken, B. (2026). LabelFusion: Learning to Fuse LLMs and Transformer Classifiers for Robust Text Classification. In LREC 2026 – The Fifteenth Biennial Language Resources and Evaluation Conference: Financial Narrative Processing (FNP) Workshop. arxiv.org/abs/2512.10793
  10. Schultenkämper, S., & Bäumer, F. S. (2026). Information Disclosure on the Web: Developing a Threat Model for the Digital Twin. In Proceedings of the 2026 International Conference on Semantic Computing (ICSC) (pp. 366–371). doi.org/10.1109/ICSC67292.2026.00061
  11. Schultenkämper, S., & Bäumer, F. S. (2026). Synthetic Personas for Social Networks: Census Demographics with LLM-Generated Behavior. In International Conference on Information and Software Technologies (ICIST 2026). Springer. In Press.
  12. Vovchenko, V., Barberi, V., Schultenkämper, S., & Bäumer, F. S. (2026). ADRIAN InstructFace-Edit – Towards Robust Detection of AI-Manipulated Face Images. In The First International Conference on Security and Cybersecurity in the AI and Digital Context. DTR Society Press. In Press.
  13. Vovchenko, V., Schultenkämper, S., & Bäumer, F. S. (2026). A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models. In AIMEDIA 2026: The Second International Conference on AI-based Media Innovation. IARIA. In Press.

2025

  1. Bourdick, R., & Bäumer, F. S. (2025). Beyond Q&A: Entwicklung eines agentenbasierten Retrieval-Augmented-Generation-Systems bei der imos AG. In Informatik Festival 2025, Potsdam, 16.–19. September 2025. Potsdam. doi.org/10.18420/inf2025_113
  2. Bäumer, F. S., Brandt-Pook, H., Huppert, C., Šir, Y., Stahlkopf, A., Sang, F., & Ullrich, N. (2025). Für eine inklusive digitale Zukunft: Ableismus-sensible Chatbots gestalten. In KI2025@HSBI Zukunft im Fokus, Bd. 2. Bielefeld. doi.org/10.60802/sidas.2025.2
  3. Kant, G., Alkhayer, T., Kivimäki, T., & Weisser, C. (2025). The Word and the Bullet: Out-Grouping and Threat Framing as Predictors of Islamic State Targeting, 2015–2020. Terrorism and Political Violence. doi.org/10.1080/09546553.2025.2561194
  4. Paredes Amorin, A., Python, A., & Weisser, C. (2025). Fine-Tuning of Lightweight Large Language Models for Sentiment Classification on Heterogeneous Financial Textual Data. arXiv. arxiv.org/abs/2512.00946
  5. Reuter, A., Thielmann, A., Weisser, C., Säfken, B., & Kneib, T. (2025). Probabilistic Topic Modeling With Transformer Representations. IEEE Transactions on Neural Networks and Learning Systems, 36(8), 14551–14565. doi.org/10.1109/TNNLS.2025.3538262
  6. Schultenkämper, S., & Bäumer, F. S. (2025). Multimodal Analysis of Modern Phishing Attacks: Towards an Evolving Dataset of Real-World Data. In Proceedings of the 2025 19th International Conference on Semantic Computing (ICSC) (pp. 265–270). doi.org/10.1109/ICSC64641.2025.00046
  7. Schultenkämper, S., Vovchenko, V., & Bäumer, F. S. (2025). Enhancing Social Media Summarization with Instruction Tuned Large Language Models and Adaptive Summary Ranking. In Proceedings of the 2025 19th International Conference on Semantic Computing (ICSC) (pp. 259–264). doi.org/10.1109/ICSC64641.2025.00045
  8. Semnani, P., Bogojeski, M., Bley, F., Zhang, Z., Wu, Q., Kneib, T., Herrmann, J., Weisser, C., Patcas, F., & Müller, K.-R. (2025). From Prediction to Proposal in Catalysis: Robust Evaluation, LRP Explanations, and Relevance-Guided Candidate Generation. In European Conference on Neural Information Processing Systems (EurIPS) Workshop: SimBioChem 2025. openreview.net/forum?id=KYt62m0WXv

2024

  1. Bäumer, F. S., Brandt-Pook, H., Matutat, A., Maoro, F., Pelkmann, D., & Schultenkämper, S. (2024). Lektionen und Anwendungsfälle aus der Implementierung von Retrieval-Augmented-Generation-Systemen. In M. Klein, D. Krupka, C. Winter, M. Gergeleit, L. Martin, & Gesellschaft für Informatik e.V. (GI) (Eds.), INFORMATIK 2024 (Vol. 352). Berlin: Köllen Druck+Verlag GmbH. doi.org/10.18420/inf2024_146
  2. Bäumer, F. S., Schultenkämper, S., Geierhos, M., & Lee, Y. S. (2024). Mirroring Privacy Risks with Digital Twins: When Pieces of Personal Data Suddenly Fit Together. SN Computer Science, 5(8). doi.org/10.1007/s42979-024-03413-z
  3. Damm, H., Pakull, T. M. G., Eryılmaz, B., Becker, H., Idrissi-Yaghir, A., Schäfer, H., Schultenkämper, S., & Friedrich, C. M. (2024). WisPerMed at „Discharge Me!“: Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV. In D. Demner-Fushman, S. Ananiadou, M. Miwa, K. Roberts, & J. Tsujii (Eds.), Proceedings of the 23rd Workshop on Biomedical Natural Language Processing (pp. 105–121). Stroudsburg, PA, USA: Association for Computational Linguistics. doi.org/10.18653/v1/2024.bionlp-1.9
  4. Kant, G., Zhelyazkov, I., Thielmann, A., Weisser, C., Schlee, M., Ehrling, C., Säfken, B., & Kneib, T. (2024). One-Way Ticket to the Moon? An NLP-Based Insight on the Phenomenon of Small-Scale Neo-Broker Trading. Social Network Analysis and Mining, 14(1), Article 121. doi.org/10.1007/s13278-024-01273-2
  5. Maoro, F., & Geierhos, M. (2024). Vertrauenswürdige Künstliche Intelligenz für polizeiliche Anwendungen. In Kongress KI@HSBI2023 – Solutions im Fokus – Posterbeiträge (Nr. 1/2024). Schriftenreihe des Institute for Data Science Solutions. doi.org/10.60802/sidas.2024.1
  6. Reuter, A., Khadka, B., Thielmann, A., Weisser, C., Fischer, S., & Säfken, B. (2024). GPTopic: Dynamic and Interactive Topic Representations. arXiv. arxiv.org/abs/2403.03628
  7. Schultenkämper, S., & Bäumer, F. S. (2024). Pixels versus Privacy: Leveraging Vision-Language Models for Sensitive Information Extraction. International Journal on Advances in Security, 17. IARIA Journals. personales.upv.es/thinkmind/dl/journals/sec/sec_v17_n12_2024/sec_v17_n12_2024_1.pdf
  8. Schultenkämper, S., & Bäumer, F. S. (2024). Structured Knowledge Extraction for Digital Twins: Leveraging LLMs to Analyze Tweets. In F. Phillipson, G. Eichler, C. Erfurth, & G. Fahrnberger (Eds.), Innovations for Community Services. I4CS 2024. Communications in Computer and Information Science (Vol. 2109, pp. 150–165). Cham: Springer. doi.org/10.1007/978-3-031-60433-1_10
  9. Semnani, P., Bogojeski, M., Bley, F., Zhang, Z., Wu, Q., Kneib, T., Herrmann, J., Weisser, C., Patcas, F., & Müller, K.-R. (2024). A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery. The Journal of Physical Chemistry C, 128(50), 21349–21367. doi.org/10.1021/acs.jpcc.4c05332
  10. Stemmler, H., Lenel, F., Kis-Katos, K., & Weisser, C. (2024). Solar Technology as a Shock-Coping Device: Evidence from Rural Tanzania. SSRN. doi.org/10.2139/ssrn.5046518
  11. Thielmann, A. F., Kumar, M., Weisser, C., Reuter, A., Säfken, B., & Samiee, S. (2024). Mambular: A Sequential Model for Tabular Deep Learning. arXiv. arxiv.org/abs/2408.06291
  12. Thielmann, A., Reuter, A., Weisser, C., Kant, G., Kumar, M., & Säfken, B. (2024). STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 435–444). Bangkok, Thailand: Association for Computational Linguistics. doi.org/10.18653/v1/2024.acl-short.41
  13. Thielmann, A., Weisser, C., & Säfken, B. (2024). Human in the Loop: How to Effectively Create Coherent Topics by Manually Labelling Only a Few Documents per Class. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 8395–8405). aclanthology.org/2024.lrec-main.736/

2023

  1. Bäumer, F. S., Brandt-Pook, H., Maoro, F., Schultenkämper, S., & Stecker, B. (2023). Von der Theorie zur Praxis: Erfahrungen bei der akademischen Begleitung von KI-Projekten in KMUs. In M. Klein, D. Krupka, C. Winter, V. Wohlgemuth, & Gesellschaft für Informatik e.V. (GI) (Eds.), INFORMATIK 2023 (Vol. 337). Berlin: Köllen Druck+Verlag GmbH. doi.org/10.18420/inf2023_184
  2. Bäumer, F. S., Chen, W.-F., Geierhos, M., Kersting, J., & Wachsmuth, H. (2023). Dialogue-Based Requirement Compensation and Style-Adjusted Data-To-Text Generation. Zenodo. doi.org/10.5281/zenodo.8068456
  3. Schultenkämper, S., & Bäumer, F. S. (2023). Looking for a Needle in a Haystack: How Can Vision-Language Understanding Help to Identify Privacy-Threatening Images on the Web. In Proceedings of the Eighteenth International Conference on Internet and Web Applications and Services (ICIW 2023). IARIA.
  4. Schultenkämper, S., & Bäumer, F. S. (2023). Privacy Risks in German Patient Forums: A NER-based Approach to Enrich Digital Twins. In Information and Software Technologies. 29th International Conference, ICIST 2023, Kaunas, Lithuania, October 12–14, 2023, Proceedings (pp. 113–123). Cham: Springer International Publishing. doi.org/10.1007/978-3-031-48981-5_9
  5. Schultenkämper, S., & Bäumer, F. S. (2023). Protecting Your Online Privacy: Insights on Digital Twins and Threat Detection. In The Fifteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2023). Special Track DaMIA: Data Mining in Industrial Applications of Digital Twins. IARIA.
  6. Schultenkämper, S., Bäumer, F. S., Geierhos, M., & Lee, Y. S. (2023). From Unstructured Data to Digital Twins: From Tweets to Structured Knowledge. In Proceedings of the Thirteenth International Conference on Social Media Technologies, Communication, and Informatics (SOTICS 2023). Valencia, Spain.
  7. Schultenkämper, S., Bäumer, F. S., Lee, Y. S., & Geierhos, M. (2023). From Digital Tracks to Digital Twins: On the Path to Cross-Platform Profile Linking. In 27th International Conference on Enterprise Design, Operations Computing (EDOC 2023): First Workshop on the Modelling and Implementation of Digital Twins for Complex Systems (pp. 158–171). Groningen, The Netherlands. doi.org/10.1007/978-3-031-54712-6_10
  8. Sirvend, B., Stahlkopf, A., & Bäumer, F. S. (2023). Synergien zwischen KI und menschlicher Expertise: Online-Sichtbarkeitsoptimierung durch Text-Transformation. In M. Klein, D. Krupka, C. Winter, & V. Wohlgemuth (Eds.), INFORMATIK 2023 (Vol. 337). Berlin: Köllen Druck+Verlag GmbH. doi.org/10.18420/inf2023_185
  9. Stecker, B., & Brandt-Pook, H. (2023). Application of Object Detection Models for the Detection of Kitchen Furniture – A Comparison. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing. ICAISC 2023. Lecture Notes in Computer Science (Vol. 14126). Cham: Springer. doi.org/10.1007/978-3-031-42508-0_9
  10. Svanidze, D., Python, A., Weisser, C., Säfken, B., Kneib, T., & Fu, J. (2023). The Road to Reproducible Research: Hazards to Avoid and Tools to Get You There Safely. Real World Data Science. realworlddatascience.net/applied-insights/case-studies/posts/2023/06/15/road-to-reproducible-research.html
  11. Thielmann, A., Weisser, C., Kneib, T., & Säfken, B. (2023). Coherence Based Document Clustering. In 2023 IEEE 17th International Conference on Semantic Computing (ICSC) (pp. 9–16). IEEE. doi.org/10.1109/ICSC56153.2023.00009
  12. Weisser, C., Gerloff, C., Thielmann, A., Python, A., Reuter, A., Kneib, T., & Säfken, B. (2023). Pseudo-Document Simulation for Comparing LDA, GSDMM and GPM Topic Models on Short and Sparse Text Using Twitter Data. Computational Statistics, 38(2), 647–674. doi.org/10.1007/s00180-022-01246-z

2022

  1. Buchmüller, A., Kant, G., Weisser, C., Säfken, B., Kis-Katos, K., & Kneib, T. (2022). Twitmo: A Twitter Data Topic Modeling and Visualization Package for R. arXiv. arxiv.org/abs/2207.11236
  2. Bäumer, F. S., Denisov, S., Sirvend, B., & Weber, J. (2022). Tackling the „We have no Data“ Challenge: Domain-Specific Machine Translation in SMEs. In Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence – Data Science for Real-World Applications. PATTERNS.
  3. Caron, M., Bäumer, F. S., & Müller, O. (2022). Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models. In Proceedings of the 55th Hawaii International Conference on System Sciences. Maui, Hawaii. doi.org/10.24251/HICSS.2022.098
  4. Denisov, S., & Bäumer, F. S. (2022). The Only Link You'll Ever Need: How Social Media Reference Landing Pages speed up Profile Matching. In Information and Software Technologies: 28th International Conference, ICIST 2022, Kaunas, Lithuania, 13th–15th October, 2022, Proceedings (pp. 136–147). Kaunas, Lithuania: Springer. doi.org/10.1007/978-3-031-16302-9_10
  5. Denisov, S., Bäumer, F. S., & Geierhos, M. (2022). Track Me If You Can: Insights into Profile Interlinking on Social Networks. In Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence – Data Science for Real-World Applications. PATTERNS.
  6. Kant, G., Weisser, C., Kneib, T., & Säfken, B. (2022). Topic Model – Machine Learning Classifier Integrations on Geocoded Twitter Data. In N. H. Phuong, & V. Kreinovich (Eds.), Biomedical and Other Applications of Soft Computing. Studies in Computational Intelligence (pp. 105–120). Cham: Springer. doi.org/10.1007/978-3-031-08580-2_11
  7. Kant, G., Wiebelt, L., Weisser, C., Kis-Katos, K., Luber, M., & Säfken, B. (2022). An Iterative Topic Model Filtering Framework for Short and Noisy User-Generated Data: Analyzing Conspiracy Theories on Twitter. International Journal of Data Science and Analytics. doi.org/10.1007/s41060-022-00321-4
  8. Kersting, J., & Bäumer, F. S. (2022). Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis. In J. Kersting (Ed.), Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence – Data Science for Real-World Applications. PATTERNS.
  9. Seufert, J. D., Python, A., Weisser, C., Cisneros, E., Kis-Katos, K., & Kneib, T. (2022). Mapping Ex Ante Risks of COVID-19 in Indonesia Using a Bayesian Geostatistical Model on Airport Network Data. Journal of the Royal Statistical Society Series A: Statistics in Society, 185(4), 2121–2155. doi.org/10.1111/rssa.12866
  10. Spoor, J. M., Weber, J., Hagemann, S., & Bäumer, F. S. (2022). Concept of an Inference Procedure for Fault Detection in Production Planning. In Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence – Data Science for Real-World Applications. PATTERNS.
  11. Tillmann, A., Kqiku, L., Reinhardt, D., Weisser, C., Säfken, B., & Kneib, T. (2022). Privacy Estimation on Twitter: Modelling the Effect of Latent Topics on Privacy by Integrating XGBoost, Topic and Generalized Additive Models. In Proceedings of the 2022 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (pp. 2325–2332). IEEE. doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00365

2021

  1. Kruse, R.-M., Säfken, B., Silbersdorff, A., & Weisser, C. (Eds.) (2021). Learning Deep Textwork: Perspectives on Natural Language Processing and Artificial Intelligence. Göttingen: Göttingen University Press. doi.org/10.17875/gup2021-1608
  2. Bäumer, F. S., Denisov, S., Lee, Y. S., & Geierhos, M. (2021). Towards Authority-Dependent Risk Identification and Analysis in Online Networks. In Proceedings of Artificial Intelligence, Machine Learning and Big Data for Hybrid Military Operations (AI4HMO), 5–6 October, Online. NATO Science and Technology Organization. www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-IST-190/MP-IST-190-24.pdf
  3. Bäumer, F. S., Kersting, J., Denisov, S., & Geierhos, M. (2021). In Other Words: A Naive Approach to Text Spinning. In Proceedings of the 18th International Conference on Applied Computing. Lisbon, Portugal: IADIS.
  4. Denisov, S., & Bäumer, F. S. (2021). Evaluation of Named Entity Recognition for the German E-Commerce Domain. In Proceedings of the 18th International Conference on Applied Computing. Lisbon, Portugal: IADIS.
  5. Denisov, S., & Bäumer, F. S. (2021). How to Improve E-commerce Search Engines? Evaluating Transformer-based Named Entity Recognition on German Product Datasets. In Information and Software Technologies: 27th International Conference, ICIST 2021, Kaunas, Lithuania, 14th–16th October, 2021, Proceedings (pp. 353–366). Kaunas, Lithuania: Springer. doi.org/10.1007/978-3-030-88304-1_28
  6. Hötte, D. A., Bäumer, F. S., Biallaß, I. D., & Sommerfeld, M. (2021). Die Unterstützung der Arbeit auf der Rechtsantragstelle durch Chatbots. Computer und Recht, 37(11), 770–776. doi.org/10.9785/cr-2021-371118
  7. Klat, W., & Brandt-Pook, H. (2021). A Blueprint for Computer Vision Testing in the Circular Economy. In V. Wohlgemuth, S. Naumann, H.-K. Arndt, & G. Behrens (Eds.), Environmental Informatics. A bogeyman or saviour to achieve the UN Sustainable Development Goals? Proceedings of the 35th edition of the EnviroInfo (pp. 251–258).
  8. Luber, M., Thielmann, A., Weisser, C., & Säfken, B. (2021). Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models. arXiv. arxiv.org/abs/2111.10401
  9. Luber, M., Weisser, C., Säfken, B., Silbersdorff, A., Kneib, T., & Kis-Katos, K. (2021). Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data. In J. Bright, A. Giachanou, V. Spaiser, A. George, & A. Pavliuc (Eds.), Disinformation in Open Online Media. Lecture Notes in Computer Science (Vol. 12887, pp. 33–49). Cham: Springer. doi.org/10.1007/978-3-030-87031-7_3
  10. Thielmann, A., Weisser, C., & Krenz, A. (2021). One-Class Support Vector Machine and LDA Topic Model Integration – Evidence for AI Patents. In N. H. Phuong, & V. Kreinovich (Eds.), Soft Computing for Biomedical and Related Applications. Studies in Computational Intelligence (Vol. 981, pp. 263–272). Cham: Springer. doi.org/10.1007/978-3-030-76620-7_23
  11. Thielmann, A., Weisser, C., Krenz, A., & Säfken, B. (2021). Unsupervised Document Classification Integrating Web Scraping, One-Class SVM and LDA Topic Modelling. Journal of Applied Statistics, 50(3), 574–591. doi.org/10.1080/02664763.2021.1919063
  12. Thormann, M.-L., Farchmin, J., Weisser, C., Kruse, R.-M., Säfken, B., & Silbersdorff, A. (2021). Stock Price Predictions with LSTM Neural Networks and Twitter Sentiment. Statistics, Optimization & Information Computing, 9(2), 268–287. doi.org/10.19139/soic-2310-5070-1202
  13. Tillmann, A., Thielmann, A., Kant, G., Weisser, C., Säfken, B., Silbersdorff, A., & Kneib, T. (2021). AuDoLab: Automatic Document Labelling and Classification for Extremely Unbalanced Data. Journal of Open Source Software, 6(66), 3719. doi.org/10.21105/joss.03719
  14. Weisser, C., Lenel, F., Lu, Y., Kis-Katos, K., & Kneib, T. (2021). Using Solar Panels for Business Purposes: Evidence Based on High-Frequency Power Usage Data. Development Engineering, 6, 100074. doi.org/10.1016/j.deveng.2021.100074

2020

  1. Säfken, B., Silbersdorff, A., & Weisser, C. (Eds.) (2020). Learning Deep: Perspectives on Deep Learning Algorithms and Artificial Intelligence. Göttingen: Göttingen University Press. doi.org/10.17875/gup2020-1338
  2. Bäumer, F. S., Kersting, J., Buff, B., & Geierhos, M. (2020). Tag Me If You Can: Insights into the Challenges of Supporting Unrestricted P2P News Tagging. In Information and Software Technologies: 26th International Conference, ICIST 2020, Kaunas, Lithuania, 15th–17th October, 2020, Proceedings (pp. 368–382). Kaunas, Lithuania: Springer. doi.org/10.1007/978-3-030-59506-7_30
  3. Gelwer, E., Weber, J., & Bäumer, F. S. (2020). A Concept of enabling Data Consistency Checks between Production and Production Planning using AI. In Proceedings of the 17th International Conference on Applied Computing. Lisbon, Portugal: IADIS.
  4. Gelwer, E., Weber, J., & Bäumer, F. S. (2020). Data Consistency from Production to Production Planning using AI. An Insight. In Proceedings of the 17th International Conference on Applied Computing. Lisbon, Portugal: IADIS.
  5. Kant, G., Weisser, C., & Säfken, B. (2020). TTLocVis: A Twitter Topic Location Visualization Package. Journal of Open Source Software, 5(54), 2507. doi.org/10.21105/joss.02507
  6. Kersting, J., & Bäumer, F. S. (2020). Semantic Tagging of Requirement Descriptions: A Transformer-based Approach. In Proceedings of the 17th International Conference on Applied Computing. Lisbon, Portugal: IADIS.

2019

  1. Bäumer, F. S., & Buff, B. (2019). How to Boost Customer Relationship Management via Web Mining Benefiting from the Glass Customer's Openness. In Proceedings of the 8th International Conference on Data Science, Technology and Applications – Volume 1: DATA (pp. 129–136). SciTePress. doi.org/10.5220/0007828301290136
  2. Bäumer, F. S., Buff, B., & Geierhos, M. (2019). Potentielle Privatsphäreverletzungen aufdecken und automatisiert sichtbar machen. In P. Sahle (Ed.), DHd 2019 Digital Humanities: multimedial & multimodal. Konferenzabstracts (pp. 192–193). Frankfurt am Main, Germany: Zenodo. doi.org/10.5281/zenodo.4622005
  3. Bäumer, F. S., Kersting, J., & Geierhos, M. (2019). Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches. Computers, 8(1). doi.org/10.3390/computers8010022
  4. Kersting, J., Bäumer, F. S., & Geierhos, M. (2019). In Reviews We Trust: But Should We? Experiences with Physician Review Websites. In M. Ramachandran, R. Walters, G. Wills, V. Méndez Muñoz, & V. Chang (Eds.), Proceedings of the 4th International Conference on Internet of Things, Big Data and Security (pp. 147–155). Heraklion, Greece: SciTePress – Science and Technology Publications. doi.org/10.5220/0007745401470155

2018

  1. Bäumer, F. S., & Geierhos, M. (2018). Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-based Configuration of Text Analysis Pipelines. In Proceedings of the 51st Hawaii International Conference on System Sciences (pp. 5746–5755). Big Island, Waikoloa Village. hdl.handle.net/10125/50609
  2. Bäumer, F. S., & Geierhos, M. (2018). How to Deal with Inaccurate Service Descriptions in On-The-Fly Computing: Open Challenges. In M. Silberztein, F. Atigui, E. Kornyshova, E. Métais, & F. Meziane (Eds.), Proceedings of the 23rd International Conference on Natural Language and Information Systems (Vol. 10859, pp. 509–513). Cham, Switzerland: Springer. doi.org/10.1007/978-3-319-91947-8_53
  3. Bäumer, F. S., & Geierhos, M. (2018). How to Deal with Inaccurate Service Requirements? Insights in Our Current Approach and New Ideas. In K. Schmid, P. Spoletini, E. Ben Charrada, Y. Chisik, F. Dalpiaz, A. Ferrari, & A. Zamansky (Eds.), Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018) (Vol. 2075). Utrecht, The Netherlands: CEUR-WS.org. ceur-ws.org/Vol-2075/NLP4RE_paper1.pdf
  4. Bäumer, F. S., & Geierhos, M. (2018). NLP in OTF Computing: Current Approaches and Open Challenges. In R. Damaševičius, & G. Vasiljevienė (Eds.), Proceedings of the 24th International Conference on Information and Software Technologies (ICIST 2018) (Vol. 920, pp. 559–570). Cham, Switzerland: Springer. doi.org/10.1007/978-3-319-99972-2_46
  5. Bäumer, F. S., & Geierhos, M. (2018). Text Broom: A ML-based Tool to Detect and Highlight Privacy Breaches in Physician Reviews: An Insight into Our Current Work. In European Conference on Data Analysis 2018: Multidisciplinary Facets of Data Science – Book of Abstracts. Paderborn, Germany.
  6. Bäumer, F. S., Kersting, J., Kuršelis, V., & Geierhos, M. (2018). Rate Your Physician: Findings from a Lithuanian Physician Rating Website. In R. Damaševičius, & G. Vasiljevienė (Eds.), Communications in Computer and Information Science (Vol. 920, pp. 43–58). Cham, Switzerland: Springer. doi.org/10.1007/978-3-319-99972-2_4
  7. Bäumer, F. S., Kersting, J., Orlikowski, M., & Geierhos, M. (2018). Towards a Multi-Stage Approach to Detect Privacy Breaches in Physician Reviews. In A. Khalili, & M. Koutraki (Eds.), Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems co-located with the 14th International Conference on Semantic Systems (SEMANTiCS 2018) (Vol. 2198). Vienna, Austria: CEUR-WS.org. ceur-ws.org/Vol-2198/paper_97.pdf
  8. Caron, M., Bäumer, F. S., & Geierhos, M. (2018). Back to Basics: Extracting Software Requirements with a Syntactic Approach. In K. Schmid, P. Spoletini, E. Ben Charrada, Y. Chisik, F. Dalpiaz, A. Ferrari, & A. Zamansky (Eds.), Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018) (Vol. 2075). Utrecht, The Netherlands: CEUR-WS.org. ceur-ws.org/Vol-2075/NLP4RE_paper5.pdf
  9. Friesen, E., Bäumer, F. S., & Geierhos, M. (2018). CORDULA: Software Requirements Extraction Utilizing Chatbot as Communication Interface. In K. Schmid, P. Spoletini, E. Ben Charrada, Y. Chisik, F. Dalpiaz, A. Ferrari, & A. Zamansky (Eds.), Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018) (Vol. 2075). Utrecht, The Netherlands: CEUR-WS.org. ceur-ws.org/Vol-2075/NLP4RE_paper3.pdf