TTLab – Text Technology Lab

The TTLab (Text Technology Lab), headed by Prof. Alexander Mehler, is part of the Department of Computer Science and Mathematics (Fachbereich Informatik und Mathematik) at the Goethe Universität in Frankfurt. It investigates formal, algorithmic models to deepen our understanding of information processing in the humanities. We examine diachronic, time-dependent as well as synchronic aspects of processing linguistic and non-linguistic, multimodal signs. The Lab works across several disciplines to bridge between computer science on the one hand and corpus-based research in the humanities on the other. To this end, we develop information models and algorithms for the analysis of texts, images, and other objects relevant to research in the humanities.

News

  • New Publication at AACL-IJCNLP 2026

    by

    Leon Hammerla

    We are pleased to announce the acceptance of the paper “From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks” in the proceedings of the 5th Asia-Pacific Chapter of the Association for Computational Linguistics & the 15th International Joint Conference on Natural Language Processing (AACL-IJCNLP 2026)

    Ayan Datta, Mounika Marreddy, Alexander Mehler, Zhixue Zhao and Radhika Mamidi. 2026. From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks. Proceedings of the 5th Asia-Pacific Chapter of the Association for Computational Linguistics & the 15th International Joint Conference on Natural Language Processing (AACL-IJCNLP 2026). accepted.
    BibTeX 
    @inproceedings{Datta:Marreddy:Mehler:2026,
  title     = {From Early Encoding to Late Suppression: Interpreting {LLM}s on
               Character Counting Tasks},
  author    = {Datta, Ayan and Marreddy, Mounika and Mehler, Alexander and Zhao, Zhixue
               and Mamidi, Radhika},
  booktitle = {Proceedings of the 5th Asia-Pacific Chapter of the Association
               for Computational Linguistics {&} the 15th International Joint
               Conference on Natural Language Processing (AACL-IJCNLP 2026)},
  year      = {2026},
  note      = {accepted}
}

  • New Publication: Va.Si.Li-Lab in “Digitale Learning Designs”

    by

    Patrick Schrottenbacher

    We are pleased to announce the publication of our chapter “Va.Si.Li-Lab: A Virtual Reality Solution for Applications in the Field of Educational and Social Science Technologies” in the edited volume “Digitale Learning Designs für die Hochschullehre gemeinschaftlich entwickeln“. Which has been a result of the DigiTeLL Project.

    Patrick Schrottenbacher, Giuseppe Abrami, Mevlüt Bagci and Alexander Mehler. 2026. Va.Si.Li-Lab: A Virtual Reality Solution for Applications in the Field of Educational and Social Science Technologies. Digitale Learning Designs für die Hochschullehre gemeinschaftlich entwickeln: Werkzeuge – Ressourcen – Szenarien, 212–229.
    BibTeX 
    @incollection{Schrottenbacher:et:al:2026:b,
  author    = {Schrottenbacher, Patrick and Abrami, Giuseppe and Bagci, Mevl{\"u}t
               and Mehler, Alexander},
  title     = {{Va.Si.Li-Lab}: A Virtual Reality Solution for Applications in
               the Field of Educational and Social Science Technologies},
  booktitle = {Digitale Learning Designs f{\"u}r die Hochschullehre gemeinschaftlich
               entwickeln: Werkzeuge -- Ressourcen -- Szenarien},
  editor    = {Zeaiter, Sabrina and Stierwald, Mona},
  publisher = {Waxmann},
  year      = {2026},
  pages     = {212--229},
  doi       = {10.31244/9783818851170}
}

  • New publications at KONVENS 2026

    by

    Giuseppe Abrami

    We are pleased to inform you that the following papers have been accepted for presentation at KONVENS 2026, which will take place from September 14–17, 2026:

    Mevlüt Bagci, Ali Abusaleh, Daniel Baumartz, Alexander Mehler, Giuseppe Abrami and Maxim Konca. 2026. Extending a Parliamentary Corpus with MPs’ Tweets: Automatic Annotation and Evaluation Using TTLABTWEETCORPUS. KONVENS 2026 – Context matters: NLP beyond Text. accepted.
    BibTeX 
    @inproceedings{bagci:et:al:2026,
  title     = {Extending a Parliamentary Corpus with {MP}s{\textquoteright} Tweets:
               Automatic Annotation and Evaluation Using {TTLABTWEETCORPUS}},
  author    = {Mevlüt Bagci and Ali Abusaleh and Daniel Baumartz and Alexander Mehler
               and Giuseppe Abrami and Maxim Konca},
  booktitle = {KONVENS 2026 - Context matters: NLP beyond Text},
  year      = {2026},
  address   = {Hamburg (Germany)},
  keywords  = {Corpora, Tweets, Political Data, Text, Media classification},
  note      = {accepted}
}

    Christoph Wigbels, Ali Abusaleh, Markus T. Jansen, Alexander Mehler, Manuel Schaaf and Markus J. Hofmann. 2026. Individual Text Corpora Predict User-Specific Knowledge: Benchmarks of Individualized Knowledge Simulation. KONVENS 2026 – Context matters: NLP beyond Text. accepted.
    BibTeX 
    @inproceedings{Wigbels:et:al:2026:a,
  author    = {{Wigbels, Christoph and Abusaleh, Ali} and Jansen, Markus T. and Mehler, Alexander
               and Schaaf, Manuel and Hofmann, Markus J.},
  title     = {Individual Text Corpora Predict User-Specific Knowledge: Benchmarks
               of Individualized Knowledge Simulation},
  booktitle = {KONVENS 2026 - Context matters: NLP beyond Text},
  year      = {2026},
  address   = {Hamburg (Germany)},
  keywords  = {Individual Text Corpora, Retrieval-Augmented Generation, Personalized Language Models, Probabilistic Calibration, Knowledge Benchmarks, Data Contamination, German NLP, spp, circlet},
  note      = {accepted}
}

    Leon Hammerla, Bhuvanesh Verma and Alexander Mehler. 2026. RT-Seg: A Toolkit for Reasoning Trace Segmentation. KONVENS 2026 – Context matters: NLP beyond Text. accepted.
    BibTeX 
    @inproceedings{Hammerla:etal:2026:c,
  title     = {RT-Seg: A Toolkit for Reasoning Trace Segmentation},
  author    = {Leon Hammerla and Bhuvanesh Verma and Alexander Mehler},
  booktitle = {KONVENS 2026 - Context matters: NLP beyond Text},
  year      = {2026},
  address   = {Hamburg (Germany)},
  keywords  = {neglab},
  note      = {accepted}
}

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