发现新闻中被忽视的异常词，竟可预示新兴话题的出现
From Outliers to Topics in Language Models: Anticipating Trends in News Corpora
- 用语言模型嵌入+累积聚类追踪异常词演变
- 异常词随时间发展成连贯话题，跨语言一致
- 适合关注舆情预测与趋势发现的研究者
本文研究了在动态新闻语料中，常被视为噪声的异常词如何成为新兴话题的弱信号。通过使用先进语言模型的向量嵌入，并采用累积聚类方法，我们跟踪了法语和英语新闻数据集中关于企业社会责任与气候变化的主题演化。结果表明，无论在何种模型或语言下，异常词均表现出向连贯话题演化的稳定趋势。
原文摘要 · Abstract (English)
This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Using vector embeddings from state-of-the-art language models and a cumulative clustering approach, we track their evolution over time in French and English news datasets focused on corporate social responsibility and climate change. The results reveal a consistent pattern: outliers tend to evolve into coherent topics over time across both models and languages.
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