In this paper, we develop the continuous time dynamic topic model (cDTM). The
cDTM is a dynamic topic model that uses Brownian motion to model the latent
topics through a sequential collection of documents, where a "topic" is a
pattern of word use that we expect to evolve over the course of the collection.
We derive an efficient variational approximate inference algorithm that takes
advantage of the sparsity of observations in text, a property that lets us
easily handle many time points. In contrast to the cDTM, the original
discrete-time dynamic topic model (dDTM) requires that time be discretized.
Moreover, the complexity of variational inference for the dDTM grows quickly as
time granularity increases, a drawback which limits fine-grained
discretization. We demonstrate the cDTM on two news corpora, reporting both
predictive perplexity and the novel task of time stamp prediction.

本文介绍了一种新的连续时间动态主题模型（cDTM），它使用布朗运动模拟文档序列中的潜在主题，通过稀疏观测值使用变分推理算法进行有效推断。我们在两个新闻语料库上测试了该模型，并通过预测困惑度和时间戳预测等指标进行了评估。