Summary of Enhancing Topic Interpretability For Neural Topic Modeling Through Topic-wise Contrastive Learning, by Xin Gao et al.
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
by Xin Gao, Yang Lin, Ruiqing Li, Yasha Wang, Xu Chu, Xinyu Ma, Hailong Yu
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces ContraTopic, a novel neural topic model (NTM) framework that addresses the limitations of traditional NTMs in discovering interpretable insights from large datasets. Unlike existing approaches that prioritize likelihood maximization, ContraTopic incorporates contrastive learning measures to assess topic interpretability. The proposed regularizer evaluates multiple facets of topic coherence and distinctiveness throughout training, leading to topics with superior interpretability. Experimental results on three diverse datasets demonstrate the effectiveness of ContraTopic in producing more interpretable topics compared to state-of-the-art NTMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find useful information hidden in big data. Traditional methods for doing this, called neural topic models, often produce confusing and hard-to-understand results. The researchers developed a new approach called ContraTopic that helps these models create more understandable topics by considering how well each topic fits together with others. This method was tested on three different sets of data and showed significant improvement in producing topics that are easy to understand. |
Keywords
» Artificial intelligence » Likelihood
