@inproceedings{thielmann-etal-2024-human,
    title = "Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class",
    author = {Thielmann, Anton F.  and
      Weisser, Christoph  and
      S{\"a}fken, Benjamin},
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.736",
    pages = "8395--8405",
    abstract = "Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used. The code is available at the following link: https://github.com/AnFreTh/STREAM",
}

Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used. The code is available at the following link: https://github.com/AnFreTh/STREAM
Thielmann, Anton F. Weisser, Christoph S{\"a}fken, Benjamin
Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class