This work introduces a general method for automatically finding the locations
where political events in text occurred. Using a novel set of 8,000 labeled
sentences, I create a method to link automatically extracted events and
locations in text. The model achieves human level performance on the annotation
task and outperforms previous event geolocation systems. It can be applied to
most event extraction systems across geographic contexts. I formalize the
event--location linking task, describe the neural network model, describe the
potential uses of such a system in political science, and demonstrate a
workflow to answer an open question on the role of conventional military
offensives in causing civilian casualties in the Syrian civil war.

本文介绍了一种自动地从文本中找到政治事件发生地点的方法，基于一个包含 8,000 个标签的句子集合，使用了一种将自动提取的事件和文本位置联系起来的方法，该模型在注释任务上达到了人类水平表现，胜过了先前的事件地理定位系统，并可应用于大多数地理环境下的事件提取系统。本文形式化了事件 - 位置链接任务、描述了神经网络模型，并演示了一个工作流来回答叙利亚内战中传统军事攻势导致平民伤亡的开放性问题。