We introduce a new method to identify emerging concepts in large text corpora. By analyzing changes in the heatmaps of the underlying embedding space, we are able to detect these concepts with high accuracy shortly after they originate, in turn outperforming common alternatives. We further demonstrate the utility of our approach by analyzing speeches in the U.S. Senate from 1941 to 2015. Our results suggest that the minority party is more active in introducing new concepts into the Senate discourse. We also identify specific concepts that closely correlate with the Senators' racial, ethnic, and gender identities. An implementation of our method is publicly available.
View on arXiv@article{ma2025_2502.21315,
title={ Identifying Emerging Concepts in Large Corpora },
author={ Sibo Ma and Julian Nyarko },
journal={arXiv preprint arXiv:2502.21315},
year={ 2025 }
}