Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class
Anton F. Thielmann, Christoph Weisser, Benjamin Säfken
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
BibTeX
@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"
}