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    <abstract>Human-in-the-loop topic modelling incorporates users’ knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.</abstract>
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%0 Conference Proceedings
%T A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
%A Fang, Zheng
%A Alqazlan, Lama
%A Liu, Du
%A He, Yulan
%A Procter, Rob
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F fang-etal-2023-user
%X Human-in-the-loop topic modelling incorporates users’ knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.
%R 10.18653/v1/2023.eacl-main.37
%U https://aclanthology.org/2023.eacl-main.37
%U https://doi.org/10.18653/v1/2023.eacl-main.37
%P 505-522

Markdown (Informal)

[A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System](https://aclanthology.org/2023.eacl-main.37) (Fang et al., EACL 2023)

• A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System (Fang et al., EACL 2023)

ACL

• Zheng Fang, Lama Alqazlan, Du Liu, Yulan He, and Rob Procter. 2023. A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 505–522, Dubrovnik, Croatia. Association for Computational Linguistics.