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Nonparametric Bayesian Storyline Detection from Microtexts ...
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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. ... : Appeared at the Workshop on Computing News Storylines at the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP 2016) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/1601.04580 https://dx.doi.org/10.48550/arxiv.1601.04580
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24 |
A Kernel Independence Test for Geographical Language Variation ...
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26 |
The Social Dynamics of Language Change in Online Networks ...
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More emojis, less :) The competition for paralinguistic function in microblog writing
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In: First Monday; Volume 21, Number 11 - 7 November 2016 ; 1396-0466 (2016)
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29 |
Overcoming Language Variation in Sentiment Analysis with Social Attention ...
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30 |
Better Document-level Sentiment Analysis from RST Discourse Parsing ...
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36 |
Multilingual Part-of-Speech Tagging: Two Unsupervised Approaches ...
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37 |
POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
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38 |
One Vector is Not Enough: Entity-Augmented Distributional Semantics for Discourse Relations ...
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39 |
Entity-Augmented Distributional Semantics for Discourse Relations ...
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