@inproceedings{akash-etal-2022-coordinated,
title = "Coordinated Topic Modeling",
author = "Akash, Pritom Saha and
Huang, Jie and
Chang, Kevin Chen-Chuan",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.668",
doi = "10.18653/v1/2022.emnlp-main.668",
pages = "9831--9843",
abstract = "We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more interpretably by reusing existing knowledge and benefits the corpora comparison task. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects while maintaining each topic{'}s global semantics. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem. Finally, extensive experiments on multiple domains show the superiority of our model over other baselines.",
}
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%0 Conference Proceedings
%T Coordinated Topic Modeling
%A Akash, Pritom Saha
%A Huang, Jie
%A Chang, Kevin Chen-Chuan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F akash-etal-2022-coordinated
%X We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more interpretably by reusing existing knowledge and benefits the corpora comparison task. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects while maintaining each topic’s global semantics. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem. Finally, extensive experiments on multiple domains show the superiority of our model over other baselines.
%R 10.18653/v1/2022.emnlp-main.668
%U https://aclanthology.org/2022.emnlp-main.668
%U https://doi.org/10.18653/v1/2022.emnlp-main.668
%P 9831-9843
Markdown (Informal)
[Coordinated Topic Modeling](https://aclanthology.org/2022.emnlp-main.668) (Akash et al., EMNLP 2022)
ACL
- Pritom Saha Akash, Jie Huang, and Kevin Chen-Chuan Chang. 2022. Coordinated Topic Modeling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9831–9843, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.