CLEF 2019实验室概述ProtestNews：在跨背景环境中从新闻中提取抗议
Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting
摘要：
我们概述了CLEF-2019实验室ProtestNews在可推广的自然语言处理背景下从新闻中提取抗议。本实验包括文档、句子和标记级别的信息分类和提取任务，在本实验范围内分别称为任务1、任务2和任务3。这些任务要求参与者在一个或多个上述级别的跨上下文设置中从英语当地新闻中识别抗议相关信息，这在本实验的范围内是跨国的。培训和开发数据收集自印度，测试数据收集自印度和中国。实验室吸引了58支团队参与。其中12个小组和9个小组分别提交了成果和工作说明。我们已经观察到神经网络产生最好的结果，并且在中国的跨国设置中，大多数提交的性能显着下降。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
