# Geolocating Political Events in Text

## TL;DR

This paper presents a neural network-based method for automatically geolocating political events in text, achieving human-level accuracy and outperforming previous systems, with applications in political science research.

## Contribution

Introduces a novel event-location linking model trained on 8,000 labeled sentences, applicable across various geographic contexts and outperforming existing systems.

## Key findings

- Model achieves human-level performance
- Outperforms previous geolocation systems
- Demonstrates application in analyzing Syrian civil war events

## Abstract

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.