# Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting

arXiv: https://arxiv.org/abs/2008.00345 · HTML: https://mlsanity.com/paper/2008.00345/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report](https://mlsanity.com/paper/2108.07865/) (2021)
- [Event Coreference Resolution for Contentious Politics Events](https://mlsanity.com/paper/2203.10123/) (2022)
- [Global Contentious Politics Database (GLOCON) Annotation Manuals](https://mlsanity.com/paper/2206.10299/) (2022)
- [AMU-EURANOVA at CASE 2021 Task 1: Assessing the stability of multilingual BERT](https://mlsanity.com/paper/2106.14625/) (2021)
- [Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks](https://mlsanity.com/paper/2110.15599/) (2021)
- [Using Word Embeddings to Analyze Protests News](https://mlsanity.com/paper/2203.05875/) (2022)
- [Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification](https://mlsanity.com/paper/2303.12936/) (2023)
- [LLVMs4Protest: Harnessing the Power of Large Language and Vision Models for Deciphering Protests in the News](https://mlsanity.com/paper/2311.18241/) (2023)

## Abstract (arXiv)

We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing. The lab consists of document, sentence, and token level information classification and extraction tasks that were referred as task 1, task 2, and task 3 respectively in the scope of this lab. The tasks required the participants to identify protest relevant information from English local news at one or more aforementioned levels in a cross-context setting, which is cross-country in the scope of this lab. The training and development data were collected from India and test data was collected from India and China. The lab attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted results and working notes respectively. We have observed neural networks yield the best results and the performance drops significantly for majority of the submissions in the cross-country setting, which is China.