Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report

Warning The system is temporarily closed to updates for reporting purpose.

Hürriyetoğlu, Ali and Tanev, Hristo and Zavarella, Vanni and Piskorski, Jakub and Yeniterzi, Reyyan and Yörük, Erdem and Mutlu, Osman and Yüret, Deniz and Villavicencio, Aline (2021) Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report. In: Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2021) Workshop, Association for Computational Linguistics (ACL), Virtual

[thumbnail of 2021.case-1.1.pdf] PDF
2021.case-1.1.pdf
Restricted to Registered users only

Download (209kB) | Request a copy

Abstract

This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the stateof-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi-and cross-lingual machine learning in few-and zero-shot settings.
Item Type: Papers in Conference Proceedings
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Reyyan Yeniterzi
Date Deposited: 28 Aug 2021 03:37
Last Modified: 19 Aug 2022 15:35
URI: https://research.sabanciuniv.edu/id/eprint/42102

Actions (login required)

View Item
View Item