An unsupervised Bayesian modelling approach to storyline detection from news articles

Zhou, Deyu, Xu, Haiyang and He, Yulan (2015). An unsupervised Bayesian modelling approach to storyline detection from news articles. IN: EMNLP 2015 : Conference on empirical methods in natural language processing. Berwick, Robert; Korhonen, Anna; Lenci, Alessandro and et al (eds) Red Hook, NY (US): Association for Computational Linguistics.

Abstract

Storyline detection from news articles aims at summarizing events described under a certain news topic and revealing how those events evolve over time. It is a difficult task because it requires first the detection of events from news articles published in different time periods and then the construction of storylines by linking events into coherent news stories. Moreover, each storyline has different hierarchical structures which are dependent across epochs. Existing approaches often ignore the dependency of hierarchical structures in storyline generation. In this paper, we propose an unsupervised Bayesian model, called dynamic storyline detection model, to extract structured representations and evolution patterns of storylines. The proposed model is evaluated on a large scale news corpus. Experimental results show that our proposed model 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: Conference on Empirical Methods in Natural Language Processing: Sixth Workshop on Cognitive Aspects of Computational Language Learning (CogACLL-2015)
Event Title: 6th Workshop on Cognitive Aspects of Computational Language Learning
Event Type: Other
Event Dates: 2015-09-17 - 2015-09-21
Uncontrolled Keywords: Computational Theory and Mathematics,Computer Science Applications,Information Systems
Full Text Link: https://pdfs.se ... 2993.1529399396
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2015
Authors: Zhou, Deyu
Xu, Haiyang
He, Yulan ( 0000-0003-3948-5845)

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