Unsupervised storyline extraction from news articles

Zhou, Deyu, Xu, Haiyang, Dai, Xin-Yu and He, Yulan (2016). Unsupervised storyline extraction from news articles. IN: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16). Palo Alto, CA (US): AAAI.

Abstract

Storyline extraction from news streams aims to extract events under a certain news topic and reveal how those events evolve over time. It requires algorithms capable of accurately extracting events from news articles published in different time periods and linking these extracted events into coherent stories. The two tasks are often solved separately, which might suffer from the problem of error propagation. Existing unified approaches often consider events as topics, ignoring their structured representations. In this paper, we propose a non-parametric generative model to extract structured representations and evolution patterns of storylines simultaneously. In the model, each storyline is modelled as a joint distribution over some locations, organizations, persons, keywords and a set of topics. We further combine this model with the Chinese restaurant process so that the number of storylines can be determined automatically without human intervention. Moreover, per-token Metropolis-Hastings sampler based on light latent Dirichlet allocation is employed to reduce sampling complexity. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms several baseline approaches.

Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences > Computer science research group
Additional Information: -
Event Title: 25th International Joint Conference on Artificial Intelligence
Event Type: Other
Event Dates: 2016-07-09 - 2016-07-15
Uncontrolled Keywords: Artificial Intelligence
Full Text Link: http://www.ijca ... /Papers/428.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2016-07-15
Authors: Zhou, Deyu
Xu, Haiyang
Dai, Xin-Yu
He, Yulan ( 0000-0003-3948-5845)

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