A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-stories
Christos Rodosthenous, Loizos Michael, Loizos Michael
2021
Abstract
Past work on the task of identifying the geographic focus of news-stories has established that state-of-the-art performance can be achieved by using existing crowdsourced knowledge-bases. In this work we demonstrate that a further refinement of those knowledge-bases through an additional round of crowdsourcing can lead to improved performance on the aforementioned task. Our proposed methodology views existing knowledge-bases as collections of arguments in support of particular inferences in terms of the geographic focus of a given news-story. The refinement that we propose is to associate these arguments with weights — computed through crowdsourcing — in terms of how strongly they support their inference. The empirical results that we present establish the superior performance of this approach compared to the one using the original knowledge-base.
DownloadPaper Citation
in Harvard Style
Rodosthenous C. and Michael L. (2021). A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-stories.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 680-687. DOI: 10.5220/0010228406800687
in Bibtex Style
@conference{icaart21,
author={Christos Rodosthenous and Loizos Michael},
title={A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-stories},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={680-687},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010228406800687},
isbn={978-989-758-484-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-stories
SN - 978-989-758-484-8
AU - Rodosthenous C.
AU - Michael L.
PY - 2021
SP - 680
EP - 687
DO - 10.5220/0010228406800687