论文标题
2019年Lab抗议News的概述：在跨文本环境中从新闻中提取抗议活动
Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting
论文作者
论文摘要
我们介绍了CLEF-2019实验室抗议者抗议活动，内容涉及在可推广的自然语言处理的背景下从新闻中提取抗议活动。该实验室包括文档，句子和令牌级别的信息分类和提取任务，这些任务分别为任务1，任务2和任务3。这些任务要求参与者在跨境环境中以一个或多个上述水平从英语本地新闻中确定抗议相关信息，这是该实验室范围的越野。从印度收集了培训和开发数据，并从印度和中国收集了测试数据。实验室吸引了58支球队参加实验室。这些团队中的12和9分别提交了结果和工作说明。我们已经观察到神经网络会产生最佳结果，并且在越野环境（中国）中，大多数提交的表现都会显着下降。
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.
