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)


    • {Adapting Coreference Algorithms to German Fairy Tales}. Schmidt, David; Krug, Markus; Puppe, Frank. In Proceedings of the DHd 2022, M. Geierhos, P. Trilcke, I. Börner, S. Seifert, A. Busch, P. Helling (reds.). Zenodo, 2022.
    • The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales. Schmidt, David; Zehe, Albin; Lorenzen, Janne; Sergel, Lisa; D{ü}ker, Sebastian; Krug, Markus; Puppe, Frank. In Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, bll 49–56. Association for Computational Linguistics, Punta Cana, Dominican Republic (online), 2021.
    • Integrating user-specified Knowledge for semi-automatic Coreference Resolution. Schmidt, David; Krug, Markus; Puppe, Frank. Verband Digital Humanities im deutschsprachigen Raum, 2020.
    • Detecting Character References in Literary Novels using a Two Stage Contextual Deep Learning approach. Krug, Markus; Kempf, Sebastian; Schmidt, David; Weimer, Lukas; Puppe, Frank. P. Sahle; P. Helling (reds.). Zenodo, 2019.