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Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class

2022-12-19 12:57:11
Anton Thielmann, Christoph Weisser, Benjamin Säfken
arXiv_CL
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2212.09422

PDF

https://arxiv.org/pdf/2212.09422.pdf

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