Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages
This work addresses the specific problem of improving geotagging accuracy for human trafficking webpages, used by US law enforcement agencies, and is incremental as it builds on existing methods with context and constraints.
The paper tackled the problem of extracting geographical tags from human trafficking webpages, which is challenging due to unusual language models, and achieved a 28.57% improvement in precision and a 36.9% improvement in F-measure compared to a baseline method.
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.