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.

该论文总结了CLEF-2019实验室的抗议新闻提取任务，表明神经网络在信息分类方面效果最佳，但在跨国环境下，尤其是中国，大多数方案的表现显著下降。

CLEF 2019实验室ProtestNews概述：在跨环境设置中从新闻中提取抗议信息