论文标题
SCOT：随着时间的推移感认识聚类：分析词汇变化的工具
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change
论文作者
论文摘要
我们介绍了随着时间的推移的感觉聚类（SCOT），这是一种基于网络的新型工具，用于分析词汇变化。 SCOT表示单词的含义是类似单词的簇。它可视化他们的形成，改变和灭亡。探索动态网络有两种主要方法：离散的方法比较了一个从单独的时间点比较一系列聚类图。连续分析一个动态网络在一个时间跨度上的变化。 SCOT提供了一种新的混合解决方案。首先，它将时间戳记的文档汇总为间隔，并在每个离散间隔中计算一个感官图。然后，随着时间的推移，它将静态图合并到新型的动态语义邻域图。随之而来的感觉群集在连续的间隔内与模型透明度和出处的间隔内对词汇变化提供了独特的详细见解。 SCOT已成功地用于欧洲关于“危机”不断变化的含义的研究。
We present Sense Clustering over Time (SCoT), a novel network-based tool for analysing lexical change. SCoT represents the meanings of a word as clusters of similar words. It visualises their formation, change, and demise. There are two main approaches to the exploration of dynamic networks: the discrete one compares a series of clustered graphs from separate points in time. The continuous one analyses the changes of one dynamic network over a time-span. SCoT offers a new hybrid solution. First, it aggregates time-stamped documents into intervals and calculates one sense graph per discrete interval. Then, it merges the static graphs to a new type of dynamic semantic neighbourhood graph over time. The resulting sense clusters offer uniquely detailed insights into lexical change over continuous intervals with model transparency and provenance. SCoT has been successfully used in a European study on the changing meaning of `crisis'.
