Christoph Weisser
2024
Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class
Anton F. Thielmann
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Christoph Weisser
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Benjamin Säfken
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
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
STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module
Anton Thielmann
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Arik Reuter
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Christoph Weisser
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Gillian Kant
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Manish Kumar
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Benjamin Säfken
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Topic modeling is a widely used technique to analyze large document corpora. With the ever-growing emergence of scientific contributions in the field, non-technical users may often use the simplest available software module, independent of whether there are potentially better models available. We present a Simplified Topic Retrieval, Exploration, and Analysis Module (STREAM) for user-friendly topic modelling and especially subsequent interactive topic visualization and analysis. For better topic analysis, we implement multiple intruder-word based topic evaluation metrics. Additionally, we publicize multiple new datasets that can extend the so far very limited number of publicly available benchmark datasets in topic modeling. We integrate downstream interpretable analysis modules to enable users to easily analyse the created topics in downstream tasks together with additional tabular information.The code is available at the following link: https://github.com/AnFreTh/STREAM
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Co-authors
- Benjamin Säfken 2
- Anton F. Thielmann 1
- Anton Thielmann 1
- Arik Reuter 1
- Gillian Kant 1
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