An unsupervised Bayesian modelling approach to storyline detection from news articles

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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.

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Publication date2015
Publication titleEMNLP 2015 : Conference on empirical methods in natural language processing : proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning (CogACLL-2015)
EditorsRobert Berwick, Anna Korhonen, Alessandro Lenci, et al
Place of PublicationRed Hook, NY (US)
PublisherAssociation for Computational Linguistics
Pages1943-1948
Number of pages6
ISBN (Print)978-1-941643-32-7
Original languageEnglish
Event6th Workshop on Cognitive Aspects of Computational Language Learning - Lisbon, Portugal

Workshop

Workshop6th Workshop on Cognitive Aspects of Computational Language Learning
CountryPortugal
CityLisbon
Period17/09/1521/09/15
OtherWorkshop as past of the Conference on Empirical Methods in Natural Language Processing

Bibliographic note

Conference on Empirical Methods in Natural Language Processing: Sixth Workshop on Cognitive Aspects of Computational Language Learning (CogACLL-2015)

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