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.
Original language | English |
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Title of host publication | EMNLP 2015 : Conference on empirical methods in natural language processing |
Subtitle of host publication | proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning (CogACLL-2015) |
Editors | Robert Berwick, Anna Korhonen, Alessandro Lenci, et al |
Place of Publication | Red Hook, NY (US) |
Publisher | Association for Computational Linguistics |
Pages | 1943-1948 |
Number of pages | 6 |
ISBN (Print) | 978-1-941643-32-7 |
Publication status | Published - 2015 |
Event | 6th Workshop on Cognitive Aspects of Computational Language Learning - Lisbon, Portugal Duration: 17 Sept 2015 → 21 Sept 2015 |
Workshop
Workshop | 6th Workshop on Cognitive Aspects of Computational Language Learning |
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Country/Territory | Portugal |
City | Lisbon |
Period | 17/09/15 → 21/09/15 |
Other | Workshop as past of the Conference on Empirical Methods in Natural Language Processing |