Deutsch Intern
    Data Science Chair

    Jan Pfister, M.Sc.

    Chair of Data Science (Informatik X)
    University of Würzburg
    Am Hubland
    97074 Würzburg

    Email: pfister@informatik.uni-wuerzburg.de

    Phone: (+49 931)  31 - 81934

    Office: Room B106 (Computer Science Building M2)

    PGP Fingerprint: 9131 6BAF 85DE CFB3 AF89 86C9 568C 615A F18B C9C0

    Research Interests & Project

    I work in the field of Natural Language Processing (NLP) and in particular, I am interested in developing novel methods for understanding and extracting meaning from text. My work focuses on using large language models also in combination with pointer networks to capture the complexities of human language. I am currently working on applying these techniques to the task of aspect-based sentiment analysis, in order to extract fine-grained sentiment information from text.

    I joined the DMIR group for my PhD studies after receiving my master's degree in Computer Science at the University of Würzburg in 2021. In the MOTIV project, we work with voice-based digital interaction partners like Alexa. Our goal is to help users of these devices learn about potential misconceptions, mindless interactions and resulting consequences.

    This introduction was (mostly) generated by GPT3. 


    - Seminar: Ausgewählte Themen des Machine Learning (SS + WS '21)

    - Lecture: Information Retrieval (since '22)

    - Project: Machine Learning in Natural Language Processing (since '22)


    2022[ to top ]
    • Point me to your Opinion,...
      Pfister, J., Wankerl, S., Hotho, A.: Point me to your Opinion, SenPoi. Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). pp. 1313–1323. Association for Computational Linguistics, Seattle, United States (2022).
    2021[ to top ]
    • Pfister, J., Kobs, K., Hotho, A.: Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. pp. 816–825 (2021).
    2020[ to top ]
    • Emote-Controlled: Obtaini...
      Kobs, K., Zehe, A., Bernstetter, A., Chibane, J., Pfister, J., Tritscher, J., Hotho, A.: Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels. ACM Transactions on Social Computing. 3, 1–34 (2020).