TY - CHAP
T1 - A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
AU - Fang, Zheng
AU - Alqazlan, Lama
AU - Liu, Du
AU - He, Yulan
AU - Procter, Rob
N1 - Funding Information:
This work was supported in part by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1). YH is supported by a Turing AI Fellowship funded by the UK Research and Innovation (EP/V020579/1). ZF receives the PhD studentship jointly funded by the University of Warwick and China Scholarship Council.
Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85159856552&partnerID=8YFLogxK
U2 - 10.18653/v1/2023.eacl-main.37
DO - 10.18653/v1/2023.eacl-main.37
M3 - Conference paper
AN - SCOPUS:85159856552
T3 - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
SP - 505
EP - 522
BT - Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
Y2 - 2 May 2023 through 6 May 2023
ER -