2021

• 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. https://doi.org/10.17875/gup2021-1608
• Aydin, M., Weisser, C., Rué, O., Mariadassou, M., Maaß, S., Behrendt, A.-K., Jaszczyszyn, Y., Heilker, T., Spaeth, M., Vogel, S., Lutz, S., Ahmad-Nejad, P., Graf, V., Bellm, A., Weisser, C., Naumova, E. A., Arnold, W. H., Ehrhardt, A., Meyer-Bahlburg, A., Becher, D., Postberg, J., Ghebremedhin, B., & Wirth, S. (2021). The Rhinobiome of Exacerbated Wheezers and Asthmatics: Insights From a German Pediatric Exacerbation Network. Frontiers in Allergy, 2. https://doi.org/10.3389/falgy.2021.667562
• 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. https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-IST-190/MP-IST-190-24.pdf
• 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.
• 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.
• 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. https://doi.org/10.1007/978-3-030-88304-1_28
• 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. https://doi.org/10.9785/cr-2021-371118
• 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).
• Luber, M., Thielmann, A., Weisser, C., & Säfken, B. (2021). Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models. arXiv. https://arxiv.org/abs/2111.10401
• 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. https://doi.org/10.1007/978-3-030-87031-7_3
• 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. https://doi.org/10.1007/978-3-030-76620-7_23
• 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. https://doi.org/10.1080/02664763.2021.1919063
• 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. https://doi.org/10.19139/soic-2310-5070-1202
• 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. https://doi.org/10.21105/joss.03719
• 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. https://doi.org/10.1016/j.deveng.2021.100074