News events and social media are composed of evolving storylines, which
capture public attention for a limited period of time. Identifying storylines
requires integrating temporal and linguistic information, and prior work takes
a largely heuristic approach. We present a novel online non-parametric Bayesian
framework for storyline detection, using the distance-dependent Chinese
Restaurant Process (dd-CRP). To ensure efficient linear-time inference, we
employ a fixed-lag Gibbs sampling procedure, which is novel for the dd-CRP. We
evaluate on the TREC Twitter Timeline Generation (TTG), obtaining encouraging
results: despite using a weak baseline retrieval model, the dd-CRP story
clustering method is competitive with the best entries in the 2014 TTG task.

我们用基于距离依赖的中文饭店过程（dd-CRP）提出了故事情节检测的一个新的在线非参数贝叶斯框架。通过使用固定滞后吉布斯采样过程来保证高效的线性推断，衡量方法在 TREC Twitter 时间线生成（TTG）上表现出色，与 2014 年 TTG 任务的最佳结果相当，尽管采用的是一个较弱的基线检索模型。