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Nonparametric Bayesian Storyline Detection from Microtexts
[article]
2016
arXiv
pre-print
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
arXiv:1601.04580v2
fatcat:bdoucrzahncijpbb3w5t2usjbq