NLP in Human Rights Research -- Extracting Knowledge Graphs About Police and Army Units and Their Commanders

TL;DR

This paper presents an NLP system designed to extract organizational and personnel data of security forces from news reports to aid human rights investigations, with promising initial performance for further development.

Contribution

The paper introduces a novel NLP system that extracts security force structures and personnel details from news reports, supporting human rights research.

Findings

System performs at a fair to good level

Sufficient performance to justify further development

Potential to save time and resources in investigations

Abstract

In this working paper we explore the use of an NLP system to assist the work of Security Force Monitor (SFM). SFM creates data about the organizational structure, command personnel and operations of police, army and other security forces, which assists human rights researchers, journalists and litigators in their work to help identify and bring to account specific units and personnel alleged to have committed abuses of human rights and international criminal law. This working paper presents an NLP system that extracts from English language news reports the names of security force units and the biographical details of their personnel, and infers the formal relationship between them. Published alongside this working paper are the system's code and training dataset. We find that the experimental NLP system performs the task at a fair to good level. Its performance is sufficient to justify further development into a live workflow that will give insight into whether its performance translates into savings in time and resource that would make it an effective technical intervention.