Deutsch Intern
    Chair of Computer Science VI - Artificial Intelligence and Applied Computer Science

    David Schmidt, M. Sc.

    Universität Würzburg
    Lehrstuhl für künstliche Intelligenz
    und Wissenssysteme
    Am Hubland
    D-97074 Würzburg 

    Room: B009
    Phone: +49 931 / 31 - 86903
    Fax: +49 931 / 31 - 86732



    About Me

    I completed my masters degree in computer science at the University of Würzburg in February 2018 and have been working at the chair for artificial intelligence since april 2018. I research the automatic generation of character networks from fairy tales and novels.

    Research Interests

    • Natural language processing

    • Information extraction

    • Coreference resolution

    • Relation extraction


    SS22 Software engineering (Coordinator of exercises)
    SS21 Medical informatics (Coordinator of exercises)
    SS20 Advanced Databases (Coordinator of exercises)
    WS 19/20 E-Learning (Coordinator of exercises)
    SS19 Software engineering (Coordinator of exercises)
    WS18/19 E-Learning (Coordinator of exercises
    SS18 Software engineering (Coordinator of exercises)


    • Schmidt, D., Krug, M., & Puppe, F. {Adapting Coreference Algorithms to German Fairy Tales}. In M. Geierhos, P. Trilcke, I. Börner, S. Seifert, A. Busch, & P. Helling (Reds), Proceedings of the DHd 2022. Zenodo. https://doi.org/10.5281/zenodo.6328165
    • Schmidt, D., Zehe, A., Lorenzen, J., Sergel, L., D{\"u}ker, S., Krug, M., & Puppe, F. The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales. Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, 49-56. https://doi.org/10.18653/v1/2021.latechclfl-1.6
    • Schmidt, D., Krug, M., & Puppe, F. Integrating user-specified Knowledge for semi-automatic Coreference Resolution. https://doi.org/10.5281/zenodo.4621748
    • Krug, M., Kempf, S., Schmidt, D., Weimer, L., & Puppe, F. Detecting Character References in Literary Novels using a Two Stage Contextual Deep Learning approach (P. Sahle & P. Helling, Reds). Zenodo. https://doi.org/10.5281/zenodo.4622060