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

Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages