  Experts combating wildlife trafficking manually sift through articles about
seizures and arrests, which is time consuming and make identifying trends
difficult. We apply natural language processing techniques to automatically
extract data from reports published by the Eco Activists for Governance and Law
Enforcement (EAGLE). We expanded Python spaCy's pre-trained pipeline and added
a custom named entity ruler, which identified 15 fully correct and 36 partially
correct events in 15 reports against an existing baseline, which did not
identify any fully correct events. The extracted wildlife trafficking events
were inserted to a database. Then, we created visualizations to display trends
over time and across regions to support domain experts. These are accessible on
our website, Wildlife Trafficking in Africa
(https://wildlifemqp.github.io/Visualizations/).
