Research Group – Applied Machine Learning Lab
The researchers of the Applied Machine Learning Lab regularly publish findings in leading journals, conference proceedings, and collaborative volumes. Below you can find an overview of our most recent and selected publications that reflect our ongoing work in Natural Language Processing (NLP) and its applications across high-impact domains such as finance, legal, medical, and psychology.
Representation Learning for NLP
Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H & Choi, Y. (2023). Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization. The Eleventh International Conference on Learning Representations (ICLR).
Link: https://openreview.net/forum?id=8aHzds2uUyB
Deußer, T., Hillebrand, L., Bauckhage, C., & Sifa, R. (2023). Informed Named Entity Recognition Decoding for Generative Language Models.
Link: https://doi.org/10.48550/arXiv.2308.07791
Wahab, A., & Sifa, R. (2021, December). DIBERT: Dependency injected bidirectional encoder representations from transformers. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8.
Link: https://doi.org/10.1109/SSCI50451.2021.9659898
NLP for Finance
Deußer, T., Leonhard, D., Hillebrand, L. P., Berger, A., Khaled, M., Heiden, S., Dilmaghani, T., Kliem, B., Loitz, R., Bauckhage, C., & Sifa, R. (2023). Uncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models. International Conference on Big Data (BigData).
Link: https://doi.org/10.24406/publica-2501
Hillebrand, L., Deußer, T., Dilmaghani, T., Kliem, B., Loitz, R., Bauckhage, C., & Sifa, R. (2022). KPI-BERT: A joint named entity recognition and relation extraction model for financial reports. 2022 26th International Conference on Pattern Recognition (ICPR), pp. 606-612.
Link: https://doi.org/10.1109/ICPR56361.2022.9956191
Sifa, R., Ladi, A., Pielka, M., Ramamurthy, R., Hillebrand, L., Kirsch, B., … & Loitz, R. (2019, September). Towards automated auditing with machine learning. In Proceedings of the ACM Symposium on Document Engineering (DocEng) 2019, pp. 1-4.
Link: https://doi.org/10.1145/3342558.3345421
Legal NLP
Hillebrand, L., Pielka, M., Leonhard, D., Deußer, T., Dilmaghani, T., Kliem, B., Loitz, R., Morad, M.J., Temath, C., Bell, T., Stenzel, R., & Sifa, R. (2023). sustain.AI: a Recommender System to analyze Sustainability Reports. Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (ICAIL).
Link: https://doi.org/10.1145/3594536.3595131
Medical Informatics
Biesner, D. , Schneider, H., Wulff, B., Attenberger, U. & Sifa, R. (2022). Improving Chest X-Ray Classification by RNN-based Patient Monitoring. 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas, 2022, pp. 946-950..
Link: https://doi.org/10.1109/ICMLA55696.2022.00158
Schneider, H., Lübbering, M., Kador, R., Broß, M., Priya, P., Biesner, D., Wulff, B., de Oliveira, T.B., Layer, Y.C., Attenberger, U. & Sifa, R. (2023). Symmetry-Aware Siamese Network: Exploiting Pathological Asymmetry for Chest X-Ray Analysis. Artificial Neural Networks and Machine Learning – ICANN 2023. Lecture Notes in Computer Science, 14257, Springer.
Link: https://doi.org/10.1007/978-3-031-44216-2_14
Luetkens, J.A., Nowak, S., Mesropyan, N. et al. (2022). Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI. Sci Rep 12, 8297.
Link: https://doi.org/10.1038/s41598-022-12410-2
Behavioral Analytics
Perry, R., Drachen, A., Kearney, A., Kriglstein, S., Nacke, L.E., Sifa, R., Wallner, G. & Johnson, D. (2018). Online-only friends, real-life friends or strangers? Differential associations with passion and social capital in video game play. Computers in Human Behavior, 79, pp. 202-210.
Link: https://doi.org/10.1016/j.chb.2017.10.032
Hadiji, F., Sifa, R., Drachen, A., Thurau, C., Kersting, K. & Bauckhage, C. (2014). Predicting player churn in the wild. 2014 IEEE Conference on Computational Intelligence and Games (CIG), Dortmund, Germany, pp. 1-8.
Link: https://doi.org/10.1109/CIG.2014.6932876
Schaekermann, M., Ribeiro, G., Wallner, G., Kriglstein, S., Johnson, D., Drachen, A., Sifa, R. & Nacke, L. E. (2017). Curiously Motivated: Profiling Curiosity with Self-Reports and Behaviour Metrics in the Game “Destiny”. Proceedings of the Annual Symposium on Computer-Human Interaction in Play 2017, New York, USA, pp. 143-156.
Link: https://doi.org/10.1145/3116595.3116603