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

TL;DR

This paper reports on a workshop and shared task focused on automating the extraction of socio-political events from news articles, highlighting the importance of cross-disciplinary approaches and evaluating various machine learning methods.

Contribution

It introduces a shared task on event sentence coreference identification and discusses the integration of computational linguistics with social sciences for large-scale socio-political event data collection.

Findings

Diverse data sources and approaches were presented.

Evaluation of machine learning methods was conducted.

The workshop highlighted the need for bridging text processing and social science requirements.

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.