@inproceedings{van-erp-etal-2014-discovering,
title = "Discovering and Visualising Stories in News",
author = "van Erp, Marieke and
Satyukov, Gleb and
Vossen, Piek and
Nijsen, Marit",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/645_Paper.pdf",
pages = "3277--3282",
abstract = "Daily news streams often revolve around topics that span over a longer period of time such as the global financial crisis or the healthcare debate in the US. The length and depth of these stories can be such that they become difficult to track for information specialists who need to reconstruct exactly what happened for policy makers and companies. We present a framework to model stories from news: we describe the characteristics that make up interesting stories, how these translate to filters on our data and we present a first use case in which we detail the steps to visualising story lines extracted from news articles about the global automotive industry.",
}
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<abstract>Daily news streams often revolve around topics that span over a longer period of time such as the global financial crisis or the healthcare debate in the US. The length and depth of these stories can be such that they become difficult to track for information specialists who need to reconstruct exactly what happened for policy makers and companies. We present a framework to model stories from news: we describe the characteristics that make up interesting stories, how these translate to filters on our data and we present a first use case in which we detail the steps to visualising story lines extracted from news articles about the global automotive industry.</abstract>
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%0 Conference Proceedings
%T Discovering and Visualising Stories in News
%A van Erp, Marieke
%A Satyukov, Gleb
%A Vossen, Piek
%A Nijsen, Marit
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F van-erp-etal-2014-discovering
%X Daily news streams often revolve around topics that span over a longer period of time such as the global financial crisis or the healthcare debate in the US. The length and depth of these stories can be such that they become difficult to track for information specialists who need to reconstruct exactly what happened for policy makers and companies. We present a framework to model stories from news: we describe the characteristics that make up interesting stories, how these translate to filters on our data and we present a first use case in which we detail the steps to visualising story lines extracted from news articles about the global automotive industry.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/645_Paper.pdf
%P 3277-3282
Markdown (Informal)
[Discovering and Visualising Stories in News](http://www.lrec-conf.org/proceedings/lrec2014/pdf/645_Paper.pdf) (van Erp et al., LREC 2014)
ACL
- Marieke van Erp, Gleb Satyukov, Piek Vossen, and Marit Nijsen. 2014. Discovering and Visualising Stories in News. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3277–3282, Reykjavik, Iceland. European Language Resources Association (ELRA).