Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion

Understand

Dynamic topic models track the evolution of topics in sequential documents, which have derived various applications like trend analysis and opinion mining.

• However, existing models suffer from repetitive topic and unassociated topic issues, failing to reveal the evolution and hindering further applications.
• To address these issues, we break the tradition of simply chaining topics in existing work and propose a novel neural \modelfullname.
• We introduce a new evolution-tracking contrastive learning method that builds the similarity relations among dynamic topics.