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Tobias Fink

Projektass. Dipl.-Ing. / BSc

Tobias Fink

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  • Overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance / Alkhalifa, R., Borkakoty, H., Deveaud, R., El-Ebshihy, A., Espinosa-Anke, L., Fink, T., Galuščáková, P., Gonzalez-Saez, G., Goeuriot, L., Iommi, D., Liakata, M., Madabushi, H. T., Medina-Alias, P., Mulhem, P., Piroi, F., Popel, M., & Zubiaga, A. (2024). Overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance. In Experimental IR Meets Multilinguality, Multimodality, and Interaction : 15th International Conference of the CLEF Association, CLEF 2024, Grenoble, France, September 9–12, 2024, Proceedings, Part II (pp. 208–230). Springer. https://doi.org/10.1007/978-3-031-71908-0_10
  • Extended overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance / Alkhalifa, R., Borkakoty, H., Deveaud, R., El-Ebshihy, A., Espinosa-Anke, L., Fink, T., Galuščáková, P., Gonzalez-Saez, G., Goeuriot, L., Iommi, D., Liakata, M., Tayyar Madabushi, H., Medina-Alias, P., Mulhem, P., Piroi, F., Popel, M., & Zubiaga, A. (2024). Extended overview of the CLEF 2024 LongEval Lab on Longitudinal Evaluation of Model Performance. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024) (pp. 2267–2289). http://hdl.handle.net/20.500.12708/210281
  • LongEval: Longitudinal Evaluation of Model Performance at CLEF 2024 / Alkhalifa, R., Borkakoty, H., Deveaud, R., El-Ebshihy, A., Espinosa-Anke, L., Fink, T., Gonzalez-Saez, G., Galuščáková, P., Goeuriot, L., Iommi, D., Liakata, M., Madabushi, H. T., Medina-Alias, P., Mulhem, P., Piroi, F., Popel, M., Servan, C., & Zubiaga, A. (2024). LongEval: Longitudinal Evaluation of Model Performance at CLEF 2024. In Advances in Information Retrieval : 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part VI (pp. 60–66). https://doi.org/10.1007/978-3-031-56072-9_8
  • Predicting Retrieval Performance Changes in Evolving Evaluation Environments / El-Ebshihy, A., Fink, T., Gonzalez-Saez, G., Galuščáková, P., Piroi, F., Iommi, D., Goeuriot, L., & Mulhem, P. (2023). Predicting Retrieval Performance Changes in Evolving Evaluation Environments. In A. Arampatzis, E. Kanoulas, T. Tsikrika, S. Vrochidis, A. Giachanou, D. Li, M. Aliannejadi, M. Vlachos, G. Faggioli, & N. Ferro (Eds.), Experimental IR Meets Multilinguality, Multimodality, and Interaction : 14th International Conference of the CLEF Association, CLEF 2023, Thessaloniki, Greece, September 18–21, 2023, Proceedings (pp. 21–33). Springer. https://doi.org/10.1007/978-3-031-42448-9_3
  • Detecting Multi Word Terms in patents the same way as entities / Fink, T., Andersson, L., & Hanbury, A. (2021). Detecting Multi Word Terms in patents the same way as entities. World Patent Information, 67, Article 102078. https://doi.org/10.1016/j.wpi.2021.102078
  • Detecting MultiWord Terms in Patents the same way as Named Entities / Fink, T., Andersson, L., & Hanbury, A. (2019). Detecting MultiWord Terms in Patents the same way as Named Entities. In Proceedings of The 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019). 1st Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2019), Karlsruhe, Germany. https://doi.org/10.34726/pst2019.3
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  • Improving multi word term detection in the patent domain with deep learning / Fink, T. (2018). Improving multi word term detection in the patent domain with deep learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.40265
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