The identification of stages in diachronic data: variability-based neighbour clustering
The identification of stages in diachronic data: variability-based neighbour clustering
复制标题
历时数据阶段的识别：基于变异性的邻居聚类
DOI：
10.3366/e1749503208000075
复制
发表时间：
2008
期刊：
影响因子：
0.5
通讯作者：
M. Hilpert
中科院分区：
文献类型：
--
作者：
S. Gries;M. Hilpert
本文提出了一种数据驱动的自底向上聚类方法，用于历时语料数据中不同阶段的定量识别。与常规分层聚类方法非常相似，它基于识别和合并最紧密的数据点组，但与常规聚类方法不同的是，它允许合并时间上相邻的数据，因此，实际上保留了时间顺序。我们用两个案例来说明这种方法，一个是关于shall的动词补语，另一个是关于英语完成时的发展。
In this paper, we introduce a data-driven bottom-up clustering method for the identification of stages in diachronic corpus data that differ from each other quantitatively. Much like regular approaches to hierarchical clustering, it is based on identifying and merging the most cohesive groups of data points, but, unlike regular approaches to clustering, it allows for the merging of temporally adjacent data, thus, in effect, preserving the chronological order. We exemplify the method with two case studies, one on verbal complementation of shall, the other on the development of the perfect in English.