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

Anton Thielmann; Christoph Weisser; Benjamin Säfken

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arXiv · 2212.09422

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

Anton Thielmann·Christoph Weisser·Benjamin Säfken

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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.

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Anton Thielmann, Christoph Weisser, Benjamin Säfken. 2022-12-19. Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class. https://arxiv.org/abs/2212.09422

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