Matthias Aßenmacher

Matthias Aßenmacher

Postdoc @ LMU Munich | BERD@NFDI | Statistician | NLP researcher

Publications

  • Lebmeier, E., Aßenmacher, M., Heumann, C. (2022). On the current state of reproducibility and reporting of uncertainty for Aspect-based Sentiment Analysis. Accepted at: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Grenoble, France, September 19-23, 2022.
  • Aßenmacher, M., Dietrich, M., Elmaklizi, A., Hemauer, E. M., Wagenknecht, N. (2022). Whitepaper: New Tools for Old Problems. Zenodo. <a target="_blank" href=https://doi.org/10.5281/zenodo.6606451>https://doi.org/10.5281/zenodo.6606451</a>.
  • Koch, P., Aßenmacher, M., Heumann, C. (2022). Pre-trained language models evaluating themselves - A comparative study. Proceedings of the Third Workshop on Insights from Negative Results in NLP, Association for Computational Linguistics, Dublin, Ireland, May 23-27, 2022. <a target="_blank" href=https://aclanthology.org/2022.insights-1.25/>https://aclanthology.org/2022.insights-1.25/</a>.
  • Aßenmacher, M., Schulze, P., Heumann, C. (2021). Benchmarking down-scaled (not so large) pre-trained language models. Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021), Düsseldorf, Germany, September 6-9, 2021. <a target="_blank" href=https://aclanthology.org/2021.konvens-1.2/>https://aclanthology.org/2021.konvens-1.2/</a>.
  • Aßenmacher, M., Corvonato, A., Heumann, C. (2021). Re-Evaluating GermEval17 Using German Pre-Trained Language Models. Proceedings of the Swiss Text Analytics Conference, Winterthur, Switzerland (Online), June 14-16, 2021. http://ceur-ws.org/Vol-2957/paper1.pdf.
  • Lebmeier, M., Hou, N., Spann, K., and Aßenmacher, M.  (2021). Creating a 'customer centricity graph' from unstructured customer feedback. Applied Marketing Analytics, 6(3), 221-229. https://www.misoda.statistik.uni-muenchen.de/forschung/lebmeier_et_al_2021.pdf.
  • Meidinger M., and Aßenmacher, M.  (2021). A new Benchmark for NLP in Social Sciences: Evaluating the usefulness of pre-trained language models for classifying open-ended survey responses. Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). Vienna, Austria (Online), February 4-6, 2021, Vol. 2: 866-873. doi.org/10.5220/0010255108660873.
  • Schiergens, T. S., Drefs, M., Dörsch, M., Kühn, F., Albertsmeier, M., Niess, H., Schoenberg, M. B., Aßenmacher, M. , Küchenhoff, H., Thasler, W.E., Guba, M. O., Angele, M. K., Rentsch, M., Werner, J., and Andrassy, J. (2021). Prognostic Impact of Pedicle Clamping during Liver Resection for Colorectal Metastases. Cancers, 13(1), 72. doi.org/10.3390/cancers13010072.
  • Viellieber V. D., and Aßenmacher, M. (2020). Pre-trained language models as knowledge bases for Automotive Complaint Analysis. arXiv preprint arXiv:2012.02558. https://arxiv.org/abs/2012.02558.
  • Guderlei M., and Aßenmacher, M. (2020). Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News. Proceedings of the 28th International Conference on Computational Linguistics (COLING). Barcelona, Spain (Online), December 8-11, 2020: 6339-6349. doi.org/10.18653/v1/2020.coling-main.558.
  • Aßenmacher, M., and Heumann, C. (2020). On the comparability of Pre-trained Language Models. Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS) Zurich, Switzerland, June 23-25, 2020. http://ceur-ws.org/Vol-2624/paper2.pdf.
  • Aßenmacher, M., Kaiser, J. C., Zaballa, I., Gasparrini, A., Küchenhoff, H. (2019). Exposure-lag-response associations between lung cancer mortality and radon exposure in German uranium miners. Radiation and Environmental Biophysics, 58(3), 321-336. doi.org/10.1007/s00411-019-00800-6.
  • Sint, A., Lutz, R., Aßenmacher, M., Küchenhoff, H., Kühn, F., Faist, E., Bazhin, A. V., Rentsch, M., Werner, J., Schiergens, T. S. (2019). Monocytic Human Leukocyte Antigen-DR Expression for Prediction of Anastomotic Leak after Colorectal Surgery. Journal of the American College of Surgeons, 229(2), 200-209. doi.org/10.1016/j.jamcollsurg.2019.03.010.
  • Deffner, V., Kreuzer, M., Sobotzki, C., Aßenmacher, M., Güthlin, D., Kaiser, C., Küchenhoff, H., Fenske, N. (2019). Uncertainties in radiation exposure assessment in the Wismut cohort: a preliminary evaluation. BIO Web of Conferences (Vol. 14, p. 03009). EDP Sciences. doi.org/10.1051/bioconf/20191403009.
  • Küchenhoff, H., Deffner, V., Aßenmacher, M., Neppl, H., Kaiser, C., & Güthlin, D. (2018). Ermittlung der Unsicherheiten der Strahlenexpositionsabschätzung in der Wismut-Kohorte-Teil I-Vorhaben 3616S12223. https://doris.bfs.de/jspui/handle/urn:nbn:​de:0221-2018080615802.
  • Brandl, C., Breinlich, V., Stark, K. J., Enzinger, S., Aßenmacher, M., Olden, M., Grassmann, F., Graw, J., Heier, M., Peters, A., Helbig, H., Küchenhoff, H., Weber, B. H. F. , Heid, I. M. (2016). Features of Age-Related Macular Degeneration in the General Adults and Their Dependency on Age, Sex, and Smoking: Results from the German KORA Study. PLOS ONE. doi:/10.1371/journal.pone.0167181.