Coordinated Topic Modeling
Pritom Saha Akash, Jie Huang, Kevin Chen–Chuan Chang · 2022
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 documentlevel 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.1