Coordinated Topic Modeling

• URL: http://arxiv.org/abs/2210.08559v1
• Date: Sun, 16 Oct 2022 15:10:54 GMT
• Title: Coordinated Topic Modeling
• Authors: Pritom Saha Akash and Jie Huang and Kevin Chen-Chuan Chang
• Abstract summary: We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem.
• Score: 10.710176350043998
• License: http://creativecommons.org/licenses/by/4.0/
• 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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