A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-Stories
Published in Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), 2021
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.
Cite this work: Christos Rodosthenous, Loizos Michael, "A Crowdsourcing Methodology for Improved Geographic Focus Identification of News-Stories." Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021), 2021.
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