# Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages

## TL;DR

This paper presents a novel ILP-based framework that combines context, constraints, and the Geonames database to improve geotag extraction accuracy from human trafficking webpages, significantly outperforming baseline methods.

## Contribution

The paper introduces an ILP-based approach integrating context and constraints with Geonames for improved geotagging in illicit domains, demonstrating substantial performance gains.

## Key findings

- Precision increased by 28.57%
- F-measure improved by 36.9%
- Framework integrated into law enforcement systems

## Abstract

Extracting geographical tags from webpages is a well-motivated application in many domains. In illicit domains with unusual language models, like human trafficking, extracting geotags with both high precision and recall is a challenging problem. In this paper, we describe a geotag extraction framework in which context, constraints and the openly available Geonames knowledge base work in tandem in an Integer Linear Programming (ILP) model to achieve good performance. In preliminary empirical investigations, the framework improves precision by 28.57% and F-measure by 36.9% on a difficult human trafficking geotagging task compared to a machine learning-based baseline. The method is already being integrated into an existing knowledge base construction system widely used by US law enforcement agencies to combat human trafficking.