Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report

• URL: http://arxiv.org/abs/2005.06070v1
• Date: Tue, 12 May 2020 22:07:14 GMT
• Title: Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report
• Authors: Ali H\"urriyeto\u{g}lu, Vanni Zavarella, Hristo Tanev, Erdem Y\"or\"uk, Ali Safaya, Osman Mutlu
• Abstract summary: We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020) We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages.
• Score: 1.9964848378974727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences.

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