Changepoint analysis deals with unsupervised detection and/or estimation of time-points in time-series data, when the distribution generating the data changes. In this article, we consider \emph{offline} changepoint detection in the context of large scale textual data. We build a specialised temporal topic model with provisions for changepoints in the distribution of topic proportions. As full likelihood based inference in this model is computationally intractable, we develop a computationally tractable approximate inference procedure. More specifically, we use sample splitting to estimate topic polytopes first and then apply a likelihood ratio statistic together with a modified version of the wild binary segmentation algorithm of Fryzlewicz et al. (2014). Our methodology facilitates automated detection of structural changes in large corpora without the need of manual processing by domain experts. As changepoints under our model correspond to changes in topic structure, the estimated changepoints are often highly interpretable as marking the surge or decline in popularity of a fashionable topic. We apply our procedure on two large datasets: (i) a corpus of English literature from the period 1800-1922 (Underwoodet al., 2015); (ii) abstracts from the High Energy Physics arXiv repository (Clementet al., 2019). We obtain some historically well-known changepoints and discover some new ones.
翻译:更改点分析涉及在发布数据变化时对时间序列数据的时间点进行未经监督的探测和/或估计的时间点, 当发布数据变化时, 我们考虑在大规模文本数据的背景下对\ emph{offline} 更改点检测。 我们建立了一个专门的时间专题模型, 规定了在主题比例分布上的变化点。 由于基于该模型的推论完全有可能是难以计算性的, 我们开发了一个可计算到的近似推论程序。 更具体地说, 我们使用样本分割来首先估算主题的多面体, 然后与修改版本的Fryzlewicz等人的野生双向分解算法一起应用概率比统计。 我们的方法有助于在不需要域专家手工处理的情况下自动检测大型蝎体体体结构的结构性变化。 由于我们模型下的变化点与主题结构的变化相对应, 估计的变更点往往非常易被解释, 以标记流行话题的激增或下降。 我们在两个大型数据集上应用了我们的程序:(i) 18-1922年期间的英国文献集, 和18— 19年的高级物理学, 的某种历史的物理(基础) 2015年的某段, 获得了20— 19 的高级物理学。