Summary of the paper
| Title | Discovering and Visualising Stories in News |
|---|---|
| Authors | Marieke Van Erp, Gleb Satyukov, Piek Vossen and Marit Nijsen |
| 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. |
| Topics | Semantic Web, Other |
Full paper  |
Discovering and Visualising Stories in News |
| Bibtex | @InProceedings{VANERP14.645, author = {Marieke Van Erp and Gleb Satyukov and Piek Vossen and Marit Nijsen}, title = {Discovering and Visualising Stories in News}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |