通过分析嵌入空间热图变化，快速识别文本中新兴概念。
Identifying Emerging Concepts in Large Corpora
- 基于嵌入空间热图的动态变化检测新兴概念
- 在1941-2015年美国参议院演讲中成功识别出新概念
- 发现少数党更活跃，且概念与身份特征高度相关
我们提出一种新方法，用于在大型文本语料库中识别新兴概念。通过分析底层嵌入空间热图的变化，能够在概念刚出现时即高精度检测，优于常见替代方法。我们进一步利用该方法分析了1941至2015年间美国参议院的演讲内容。结果表明，少数党在引入新概念方面更为积极，并识别出与议员种族、族裔及性别身份密切相关的具体概念。该方法的实现已公开提供。
原文摘要 · Abstract (English)
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.
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