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- W4298306007 abstract "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." @default.
- W4298306007 created "2022-10-02" @default.
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- W4298306007 date "2016-01-18" @default.
- W4298306007 modified "2023-09-26" @default.
- W4298306007 title "Nonparametric Bayesian Storyline Detection from Microtexts" @default.
- W4298306007 doi "https://doi.org/10.48550/arxiv.1601.04580" @default.
- W4298306007 hasPublicationYear "2016" @default.
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